In [2]:
%load_ext autoreload
%autoreload 2
In [3]:
# First, I have to laod different modules that I use for analyzing the data and for plotting:
import sys, os, collections
import pandas as pd
import numpy as np
import re
import csv
import seaborn as sns
import matplotlib.pyplot as plt; plt.rcdefaults()
from matplotlib.pyplot import figure
from collections import Counter
# Second, I have to load the Text Fabric app
from tf.fabric import Fabric
from tf.app import use
In [35]:
SBLGNTv1 = use('SBLGNT/tf/6.12_v1', hoist=globals())
This is Text-Fabric 9.1.11 Api reference : https://annotation.github.io/text-fabric/tf/cheatsheet.html 31 features found and 0 ignored
Text-Fabric: Text-Fabric API 9.1.11, no app configured
Data: D:/OneDrive/1200_AUS-research/Fabric-TEXT/0_data_SBLGNT/SBLGNT/tf/6.12_v1
Features:
Data: D:/OneDrive/1200_AUS-research/Fabric-TEXT/0_data_SBLGNT/SBLGNT/tf/6.12_v1
Features:
D:/OneDrive/1200_AUS-research/Fabric-TEXT/0_data_SBLGNT/SBLGNT/tf/6.12_v1
book
str
A book name
book_code
str
Short book abbreviation
case
str
case
chapter
int
A chapter number
clause
str
A clause number
degree
str
degree
dict_abc_order
str
words with their dictionary order
gn
str
gender
lemma
str
Dictionary entry form
lemma_dictform
str
BibleOL Greek dictionary form
lemma_freq
str
word frequency
lemma_gloss
str
BibleOL English gloss
lemma_strongs
str
Strong's number
lemma_translit
str
lemma transliteration
mood
str
mood
morph
str
Word morphological tag based on Maurice A Robinson's analysis
morphology
str
morphology
normalized_word
str
Word in its normalized form
nu
str
number
orig_order
str
number of word position in NT
otype
str
ps
str
person
sentence
int
A sentence number
tense
str
tense
text
str
Text as it appears in SBLGNT
verse
int
A verse number
voice
str
voice
vrsnum
str
N-word in verse
word
str
Word with punctuation stripped
oslots
none
Text-Fabric API: names N F E L T S C TF directly usable
This is Text-Fabric 9.1.11 Api reference : https://annotation.github.io/text-fabric/tf/cheatsheet.html 31 features found and 0 ignored | 0.10s T otype from D:/OneDrive/1200_AUS-research/Fabric-TEXT/0_data_SBLGNT/SBLGNT/tf/6.12_v2 | 0.73s T oslots from D:/OneDrive/1200_AUS-research/Fabric-TEXT/0_data_SBLGNT/SBLGNT/tf/6.12_v2 | 1.35s T text from D:/OneDrive/1200_AUS-research/Fabric-TEXT/0_data_SBLGNT/SBLGNT/tf/6.12_v2 | 0.00s T chapter from D:/OneDrive/1200_AUS-research/Fabric-TEXT/0_data_SBLGNT/SBLGNT/tf/6.12_v2 | 0.07s T verse from D:/OneDrive/1200_AUS-research/Fabric-TEXT/0_data_SBLGNT/SBLGNT/tf/6.12_v2 | 0.00s T book from D:/OneDrive/1200_AUS-research/Fabric-TEXT/0_data_SBLGNT/SBLGNT/tf/6.12_v2 | | 0.01s C __levels__ from otype, oslots, otext | | 1.09s C __order__ from otype, oslots, __levels__ | | 0.11s C __rank__ from otype, __order__ | | 2.96s C __levUp__ from otype, oslots, __rank__ | | 0.50s C __levDown__ from otype, __levUp__, __rank__ | | 8.94s C __boundary__ from otype, oslots, __rank__ | | 0.08s C __sections__ from otype, oslots, otext, __levUp__, __levels__, book, chapter, verse | 0.00s T book_code from D:/OneDrive/1200_AUS-research/Fabric-TEXT/0_data_SBLGNT/SBLGNT/tf/6.12_v2 | 1.06s T case from D:/OneDrive/1200_AUS-research/Fabric-TEXT/0_data_SBLGNT/SBLGNT/tf/6.12_v2 | 0.00s T clause from D:/OneDrive/1200_AUS-research/Fabric-TEXT/0_data_SBLGNT/SBLGNT/tf/6.12_v2 | 0.93s T degree from D:/OneDrive/1200_AUS-research/Fabric-TEXT/0_data_SBLGNT/SBLGNT/tf/6.12_v2 | 1.10s T dict_abc_order from D:/OneDrive/1200_AUS-research/Fabric-TEXT/0_data_SBLGNT/SBLGNT/tf/6.12_v2 | 1.53s T gn from D:/OneDrive/1200_AUS-research/Fabric-TEXT/0_data_SBLGNT/SBLGNT/tf/6.12_v2 | 1.44s T lemma from D:/OneDrive/1200_AUS-research/Fabric-TEXT/0_data_SBLGNT/SBLGNT/tf/6.12_v2 | 1.33s T lemma_dictform from D:/OneDrive/1200_AUS-research/Fabric-TEXT/0_data_SBLGNT/SBLGNT/tf/6.12_v2 | 1.91s T lemma_freq from D:/OneDrive/1200_AUS-research/Fabric-TEXT/0_data_SBLGNT/SBLGNT/tf/6.12_v2 | 1.98s T lemma_gloss from D:/OneDrive/1200_AUS-research/Fabric-TEXT/0_data_SBLGNT/SBLGNT/tf/6.12_v2 | 2.15s T lemma_strongs from D:/OneDrive/1200_AUS-research/Fabric-TEXT/0_data_SBLGNT/SBLGNT/tf/6.12_v2 | 1.97s T lemma_translit from D:/OneDrive/1200_AUS-research/Fabric-TEXT/0_data_SBLGNT/SBLGNT/tf/6.12_v2 | 1.75s T mood from D:/OneDrive/1200_AUS-research/Fabric-TEXT/0_data_SBLGNT/SBLGNT/tf/6.12_v2 | 1.86s T morph from D:/OneDrive/1200_AUS-research/Fabric-TEXT/0_data_SBLGNT/SBLGNT/tf/6.12_v2 | 1.62s T morphology from D:/OneDrive/1200_AUS-research/Fabric-TEXT/0_data_SBLGNT/SBLGNT/tf/6.12_v2 | 2.35s T normalized_word from D:/OneDrive/1200_AUS-research/Fabric-TEXT/0_data_SBLGNT/SBLGNT/tf/6.12_v2 | 1.98s T nu from D:/OneDrive/1200_AUS-research/Fabric-TEXT/0_data_SBLGNT/SBLGNT/tf/6.12_v2 | 2.14s T orig_order from D:/OneDrive/1200_AUS-research/Fabric-TEXT/0_data_SBLGNT/SBLGNT/tf/6.12_v2 | 1.68s T ps from D:/OneDrive/1200_AUS-research/Fabric-TEXT/0_data_SBLGNT/SBLGNT/tf/6.12_v2 | 0.00s T sentence from D:/OneDrive/1200_AUS-research/Fabric-TEXT/0_data_SBLGNT/SBLGNT/tf/6.12_v2 | 1.84s T tense from D:/OneDrive/1200_AUS-research/Fabric-TEXT/0_data_SBLGNT/SBLGNT/tf/6.12_v2 | 1.97s T voice from D:/OneDrive/1200_AUS-research/Fabric-TEXT/0_data_SBLGNT/SBLGNT/tf/6.12_v2 | 1.93s T vrsnum from D:/OneDrive/1200_AUS-research/Fabric-TEXT/0_data_SBLGNT/SBLGNT/tf/6.12_v2 | 2.68s T word from D:/OneDrive/1200_AUS-research/Fabric-TEXT/0_data_SBLGNT/SBLGNT/tf/6.12_v2
Text-Fabric: Text-Fabric API 9.1.11, no app configured
Data: D:/OneDrive/1200_AUS-research/Fabric-TEXT/0_data_SBLGNT/SBLGNT/tf/6.12_v2
Features:
Data: D:/OneDrive/1200_AUS-research/Fabric-TEXT/0_data_SBLGNT/SBLGNT/tf/6.12_v2
Features:
D:/OneDrive/1200_AUS-research/Fabric-TEXT/0_data_SBLGNT/SBLGNT/tf/6.12_v2
book
str
A book name
book_code
str
Short book abbreviation
case
str
case
chapter
int
A chapter number
clause
int
A clause number
degree
str
degree
dict_abc_order
str
words with their dictionary order
gn
str
gender
lemma
str
Lemma
lemma_dictform
str
BibleOL Greek dictionary form
lemma_freq
str
word frequency
lemma_gloss
str
BibleOL English gloss
lemma_strongs
str
Strong's number
lemma_translit
str
lemma transliteration
mood
str
mood
morph
str
Word morphological tag based on Maurice A Robinson's analysis
morphology
str
morphology
normalized_word
str
Normalized word
nu
str
number
orig_order
str
number of word position in NT
otype
str
ps
str
person
sentence
int
A sentence number
tense
str
tense
text
str
Text as it appears in SBLGNT
verse
int
A verse number
voice
str
voice
vrsnum
str
N-word in verse
word
str
Word with punctuation stripped
oslots
none
Text-Fabric API: names N F E L T S C TF directly usable
In [72]:
SBLGNTv2 = use('SBLGNT/tf/6.12_v2', hoist=globals())
This is Text-Fabric 9.1.11 Api reference : https://annotation.github.io/text-fabric/tf/cheatsheet.html 31 features found and 0 ignored | 0.20s T otype from D:/OneDrive/1200_AUS-research/Fabric-TEXT/0_data_SBLGNT/SBLGNT/tf/6.12_v2 | 1.47s T oslots from D:/OneDrive/1200_AUS-research/Fabric-TEXT/0_data_SBLGNT/SBLGNT/tf/6.12_v2 | 1.26s T text from D:/OneDrive/1200_AUS-research/Fabric-TEXT/0_data_SBLGNT/SBLGNT/tf/6.12_v2 | 0.06s T chapter from D:/OneDrive/1200_AUS-research/Fabric-TEXT/0_data_SBLGNT/SBLGNT/tf/6.12_v2 | 0.07s T verse from D:/OneDrive/1200_AUS-research/Fabric-TEXT/0_data_SBLGNT/SBLGNT/tf/6.12_v2 | 0.08s T book from D:/OneDrive/1200_AUS-research/Fabric-TEXT/0_data_SBLGNT/SBLGNT/tf/6.12_v2 | | 0.04s C __levels__ from otype, oslots, otext | | 1.44s C __order__ from otype, oslots, __levels__ | | 0.10s C __rank__ from otype, __order__ | | 3.95s C __levUp__ from otype, oslots, __rank__ | | 0.93s C __levDown__ from otype, __levUp__, __rank__ | | 7.71s C __boundary__ from otype, oslots, __rank__ | | 0.10s C __sections__ from otype, oslots, otext, __levUp__, __levels__, book, chapter, verse | 0.00s T book_code from D:/OneDrive/1200_AUS-research/Fabric-TEXT/0_data_SBLGNT/SBLGNT/tf/6.12_v2 | 1.04s T case from D:/OneDrive/1200_AUS-research/Fabric-TEXT/0_data_SBLGNT/SBLGNT/tf/6.12_v2 | 0.14s T clause from D:/OneDrive/1200_AUS-research/Fabric-TEXT/0_data_SBLGNT/SBLGNT/tf/6.12_v2 | 0.84s T degree from D:/OneDrive/1200_AUS-research/Fabric-TEXT/0_data_SBLGNT/SBLGNT/tf/6.12_v2 | 1.82s T dict_abc_order from D:/OneDrive/1200_AUS-research/Fabric-TEXT/0_data_SBLGNT/SBLGNT/tf/6.12_v2 | 1.19s T gn from D:/OneDrive/1200_AUS-research/Fabric-TEXT/0_data_SBLGNT/SBLGNT/tf/6.12_v2 | 1.90s T lemma from D:/OneDrive/1200_AUS-research/Fabric-TEXT/0_data_SBLGNT/SBLGNT/tf/6.12_v2 | 1.33s T lemma_dictform from D:/OneDrive/1200_AUS-research/Fabric-TEXT/0_data_SBLGNT/SBLGNT/tf/6.12_v2 | 1.61s T lemma_freq from D:/OneDrive/1200_AUS-research/Fabric-TEXT/0_data_SBLGNT/SBLGNT/tf/6.12_v2 | 1.38s T lemma_gloss from D:/OneDrive/1200_AUS-research/Fabric-TEXT/0_data_SBLGNT/SBLGNT/tf/6.12_v2 | 1.67s T lemma_strongs from D:/OneDrive/1200_AUS-research/Fabric-TEXT/0_data_SBLGNT/SBLGNT/tf/6.12_v2 | 1.54s T lemma_translit from D:/OneDrive/1200_AUS-research/Fabric-TEXT/0_data_SBLGNT/SBLGNT/tf/6.12_v2 | 1.67s T mood from D:/OneDrive/1200_AUS-research/Fabric-TEXT/0_data_SBLGNT/SBLGNT/tf/6.12_v2 | 1.90s T morph from D:/OneDrive/1200_AUS-research/Fabric-TEXT/0_data_SBLGNT/SBLGNT/tf/6.12_v2 | 1.78s T morphology from D:/OneDrive/1200_AUS-research/Fabric-TEXT/0_data_SBLGNT/SBLGNT/tf/6.12_v2 | 1.72s T normalized_word from D:/OneDrive/1200_AUS-research/Fabric-TEXT/0_data_SBLGNT/SBLGNT/tf/6.12_v2 | 1.99s T nu from D:/OneDrive/1200_AUS-research/Fabric-TEXT/0_data_SBLGNT/SBLGNT/tf/6.12_v2 | 1.85s T orig_order from D:/OneDrive/1200_AUS-research/Fabric-TEXT/0_data_SBLGNT/SBLGNT/tf/6.12_v2 | 1.37s T ps from D:/OneDrive/1200_AUS-research/Fabric-TEXT/0_data_SBLGNT/SBLGNT/tf/6.12_v2 | 0.12s T sentence from D:/OneDrive/1200_AUS-research/Fabric-TEXT/0_data_SBLGNT/SBLGNT/tf/6.12_v2 | 1.73s T tense from D:/OneDrive/1200_AUS-research/Fabric-TEXT/0_data_SBLGNT/SBLGNT/tf/6.12_v2 | 1.33s T voice from D:/OneDrive/1200_AUS-research/Fabric-TEXT/0_data_SBLGNT/SBLGNT/tf/6.12_v2 | 1.47s T vrsnum from D:/OneDrive/1200_AUS-research/Fabric-TEXT/0_data_SBLGNT/SBLGNT/tf/6.12_v2 | 2.34s T word from D:/OneDrive/1200_AUS-research/Fabric-TEXT/0_data_SBLGNT/SBLGNT/tf/6.12_v2
Text-Fabric: Text-Fabric API 9.1.11, no app configured
Data: D:/OneDrive/1200_AUS-research/Fabric-TEXT/0_data_SBLGNT/SBLGNT/tf/6.12_v2
Features:
Data: D:/OneDrive/1200_AUS-research/Fabric-TEXT/0_data_SBLGNT/SBLGNT/tf/6.12_v2
Features:
D:/OneDrive/1200_AUS-research/Fabric-TEXT/0_data_SBLGNT/SBLGNT/tf/6.12_v2
book
str
A book name
book_code
str
Short book abbreviation
case
str
case
chapter
int
A chapter number
clause
int
A clause number
degree
str
degree
dict_abc_order
str
words with their dictionary order
gn
str
gender
lemma
str
Lemma
lemma_dictform
str
BibleOL Greek dictionary form
lemma_freq
str
word frequency
lemma_gloss
str
BibleOL English gloss
lemma_strongs
str
Strong's number
lemma_translit
str
lemma transliteration
mood
str
mood
morph
str
Word morphological tag based on Maurice A Robinson's analysis
morphology
str
morphology
normalized_word
str
Normalized word
nu
str
number
orig_order
str
number of word position in NT
otype
str
ps
str
person
sentence
int
A sentence number
tense
str
tense
text
str
Text as it appears in SBLGNT
verse
int
A verse number
voice
str
voice
vrsnum
str
N-word in verse
word
str
Word with punctuation stripped
oslots
none
Text-Fabric API: names N F E L T S C TF directly usable
In [57]:
Searchv1_0 = '''
book book=Matthew book_code*
chapter chapter=1
sentence sentence=1
verse
'''
Searchv1_0 = SBLGNTv1.search(Searchv1_0)
SBLGNTv1.show(Searchv1_0, start=1, end=1, condensed=True, colorMap={1:'pink'}, extraFeatures={'verse','vrsnum','orig_order'})
0.01s 1 result
verse 1
Matthew 1:1
verse
verse=1
clause
βίβλος
orig_order=1vrsnum=1
γενέσεως
orig_order=2vrsnum=2
Ἰησοῦ
orig_order=3vrsnum=3
Χριστοῦ
orig_order=4vrsnum=4
υἱοῦ
orig_order=5vrsnum=5
Δαυίδ
orig_order=6vrsnum=6
υἱοῦ
orig_order=7vrsnum=7
Ἀβραάμ
orig_order=8vrsnum=8
In [76]:
Searchv2_0 = '''
book book=Matthew
chapter chapter=1
verse verse=21
'''
Searchv2_0 = SBLGNTv2.search(Searchv2_0)
SBLGNTv2.show(Searchv2_0, start=1, end=5, condensed=True, colorMap={3:'pink'}, extraFeatures={'verse','vrsnum', 'orig_order'})
0.01s 1 result
verse 1
Matthew 1:21
verse
book=Matthewchapter=1verse=21
clause
τέξεται
orig_order=349vrsnum=1
δὲ
orig_order=350vrsnum=2
υἱὸν
orig_order=351vrsnum=3
καὶ
orig_order=352vrsnum=4
καλέσεις
orig_order=353vrsnum=5
τὸ
orig_order=354vrsnum=6
ὄνομα
orig_order=355vrsnum=7
αὐτοῦ
orig_order=356vrsnum=8
Ἰησοῦν,
orig_order=357vrsnum=9
clause
αὐτὸς
orig_order=358vrsnum=10
γὰρ
orig_order=359vrsnum=11
σώσει
orig_order=360vrsnum=12
τὸν
orig_order=361vrsnum=13
λαὸν
orig_order=362vrsnum=14
αὐτοῦ
orig_order=363vrsnum=15
ἀπὸ
orig_order=364vrsnum=16
τῶν
orig_order=365vrsnum=17
ἁμαρτιῶν
orig_order=366vrsnum=18
αὐτῶν.
orig_order=367vrsnum=19
In [17]:
Search1 = '''
verse book=Matthew chapter=1 verse=1
word
'''
Search1 = SBLGNT.search(Search1)
SBLGNT.show(Search1, start=1, end=1, condensed=True, colorMap={1:'pink'}, extraFeatures={'verse','vrsnum'})
0.04s 0 results
In [15]:
Search2 = '''
book book=Revelation book_code*
chapter chapter=1
sentence
clause
word lemma* morph* normalized_word* case* degree* gn* mood* nu* orig_order* ps* voice* tense* lemma_translit* dict_abc_order* lemma_freq* lemma_dictform* lemma_freq* lemma_gloss* lemma_strongs*
'''
Search2 = SBLGNT.search(Search2)
SBLGNT.show(Search2, start=1, end=1, condensed=True, colorMap={1:'pink'}, extraFeatures={'verse','vrsnum'})
2.91s 0 results
New Feature Development¶
Extracting morphological features¶
In [381]:
featureprep=pd.read_csv('d:/OneDrive/1200_AUS-research/Fabric-TEXT/0_data_SBLGNT/feature-dev/morph_copy.tf',delimiter='\t',encoding='utf-8')
pd.set_option('display.max_columns', 50)
featureprep.head(5)
Out[381]:
| morphology | |
|---|---|
| 0 | N- ----NSF- |
| 1 | N- ----GSF- |
| 2 | N- ----GSM- |
| 3 | N- ----GSM- |
| 4 | N- ----GSM- |
In [382]:
featureprep['orig_order'] = featureprep.index +1
featureprep.head(5)
Out[382]:
| morphology | orig_order | |
|---|---|---|
| 0 | N- ----NSF- | 1 |
| 1 | N- ----GSF- | 2 |
| 2 | N- ----GSM- | 3 |
| 3 | N- ----GSM- | 4 |
| 4 | N- ----GSM- | 5 |
Adding Part of Speech sp¶
In [383]:
def spconditions(row):
if re.search('^A.*', str(row)):
return 'adj'
if re.search('^C.*', str(row)):
return 'conj'
if re.search('^D.*', str(row)):
return 'adv'
if re.search('^I.*', str(row)):
return 'interj'
if re.search('^N-.*', str(row)):
return 'noun'
if re.search('^P.*', str(row)):
return 'prep'
if re.search('^RA.*', str(row)):
return 'art-def'
if re.search('^RD.*', str(row)):
return 'pron-dem'
if re.search('^RI.*', str(row)):
return 'pron-inter'
if re.search('^RP.*', str(row)):
return 'pron-prs'
if re.search('^RR.*', str(row)):
return 'pron-rela'
if re.search('^V-.*', str(row)):
return 'verb'
if re.search('^X.*', str(row)):
return 'partcl'
else:
return ''
In [384]:
featureprep['sp']=featureprep['morphology'].apply(spconditions)
featureprep.head(20)
Out[384]:
| morphology | orig_order | sp | |
|---|---|---|---|
| 0 | N- ----NSF- | 1 | noun |
| 1 | N- ----GSF- | 2 | noun |
| 2 | N- ----GSM- | 3 | noun |
| 3 | N- ----GSM- | 4 | noun |
| 4 | N- ----GSM- | 5 | noun |
| 5 | N- ----GSM- | 6 | noun |
| 6 | N- ----GSM- | 7 | noun |
| 7 | N- ----GSM- | 8 | noun |
| 8 | N- ----NSM- | 9 | noun |
| 9 | V- 3AAI-S-- | 10 | verb |
| 10 | RA ----ASM- | 11 | art-def |
| 11 | N- ----ASM- | 12 | noun |
| 12 | N- ----NSM- | 13 | noun |
| 13 | C- -------- | 14 | conj |
| 14 | V- 3AAI-S-- | 15 | verb |
| 15 | RA ----ASM- | 16 | art-def |
| 16 | N- ----ASM- | 17 | noun |
| 17 | N- ----NSM- | 18 | noun |
| 18 | C- -------- | 19 | conj |
| 19 | V- 3AAI-S-- | 20 | verb |
Adding Gender gn¶
In [385]:
def gender(row):
if re.search('.*F.$', str(row)):
return 'f'
if re.search('.*M.$', str(row)):
return 'm'
if re.search('.*N.$', str(row)):
return 'n'
else:
return ''
In [386]:
featureprep['gn']=featureprep['morphology'].apply(gender)
featureprep.head(20)
Out[386]:
| morphology | orig_order | sp | gn | |
|---|---|---|---|---|
| 0 | N- ----NSF- | 1 | noun | f |
| 1 | N- ----GSF- | 2 | noun | f |
| 2 | N- ----GSM- | 3 | noun | m |
| 3 | N- ----GSM- | 4 | noun | m |
| 4 | N- ----GSM- | 5 | noun | m |
| 5 | N- ----GSM- | 6 | noun | m |
| 6 | N- ----GSM- | 7 | noun | m |
| 7 | N- ----GSM- | 8 | noun | m |
| 8 | N- ----NSM- | 9 | noun | m |
| 9 | V- 3AAI-S-- | 10 | verb | |
| 10 | RA ----ASM- | 11 | art-def | m |
| 11 | N- ----ASM- | 12 | noun | m |
| 12 | N- ----NSM- | 13 | noun | m |
| 13 | C- -------- | 14 | conj | |
| 14 | V- 3AAI-S-- | 15 | verb | |
| 15 | RA ----ASM- | 16 | art-def | m |
| 16 | N- ----ASM- | 17 | noun | m |
| 17 | N- ----NSM- | 18 | noun | m |
| 18 | C- -------- | 19 | conj | |
| 19 | V- 3AAI-S-- | 20 | verb |
Adding Number nu¶
In [387]:
def number(row):
if re.search('.*S..$', str(row)):
return 'sg'
if re.search('.*S[MFN].$', str(row)):
return 'sg'
if re.search('.*P..$', str(row)):
return 'pl'
if re.search('.*P[MFN].$', str(row)):
return 'pl'
else:
return ''
In [388]:
featureprep['nu']=featureprep['morphology'].apply(number)
featureprep.head(50)
Out[388]:
| morphology | orig_order | sp | gn | nu | |
|---|---|---|---|---|---|
| 0 | N- ----NSF- | 1 | noun | f | sg |
| 1 | N- ----GSF- | 2 | noun | f | sg |
| 2 | N- ----GSM- | 3 | noun | m | sg |
| 3 | N- ----GSM- | 4 | noun | m | sg |
| 4 | N- ----GSM- | 5 | noun | m | sg |
| 5 | N- ----GSM- | 6 | noun | m | sg |
| 6 | N- ----GSM- | 7 | noun | m | sg |
| 7 | N- ----GSM- | 8 | noun | m | sg |
| 8 | N- ----NSM- | 9 | noun | m | sg |
| 9 | V- 3AAI-S-- | 10 | verb | sg | |
| 10 | RA ----ASM- | 11 | art-def | m | sg |
| 11 | N- ----ASM- | 12 | noun | m | sg |
| 12 | N- ----NSM- | 13 | noun | m | sg |
| 13 | C- -------- | 14 | conj | ||
| 14 | V- 3AAI-S-- | 15 | verb | sg | |
| 15 | RA ----ASM- | 16 | art-def | m | sg |
| 16 | N- ----ASM- | 17 | noun | m | sg |
| 17 | N- ----NSM- | 18 | noun | m | sg |
| 18 | C- -------- | 19 | conj | ||
| 19 | V- 3AAI-S-- | 20 | verb | sg | |
| 20 | RA ----ASM- | 21 | art-def | m | sg |
| 21 | N- ----ASM- | 22 | noun | m | sg |
| 22 | C- -------- | 23 | conj | ||
| 23 | RA ----APM- | 24 | art-def | m | pl |
| 24 | N- ----APM- | 25 | noun | m | pl |
| 25 | RP ----GSM- | 26 | pron-prs | m | sg |
| 26 | N- ----NSM- | 27 | noun | m | sg |
| 27 | C- -------- | 28 | conj | ||
| 28 | V- 3AAI-S-- | 29 | verb | sg | |
| 29 | RA ----ASM- | 30 | art-def | m | sg |
| 30 | N- ----ASM- | 31 | noun | m | sg |
| 31 | C- -------- | 32 | conj | ||
| 32 | RA ----ASM- | 33 | art-def | m | sg |
| 33 | N- ----ASM- | 34 | noun | m | sg |
| 34 | P- -------- | 35 | prep | ||
| 35 | RA ----GSF- | 36 | art-def | f | sg |
| 36 | N- ----GSF- | 37 | noun | f | sg |
| 37 | N- ----NSM- | 38 | noun | m | sg |
| 38 | C- -------- | 39 | conj | ||
| 39 | V- 3AAI-S-- | 40 | verb | sg | |
| 40 | RA ----ASM- | 41 | art-def | m | sg |
| 41 | N- ----ASM- | 42 | noun | m | sg |
| 42 | N- ----NSM- | 43 | noun | m | sg |
| 43 | C- -------- | 44 | conj | ||
| 44 | V- 3AAI-S-- | 45 | verb | sg | |
| 45 | RA ----ASM- | 46 | art-def | m | sg |
| 46 | N- ----ASM- | 47 | noun | m | sg |
| 47 | N- ----NSM- | 48 | noun | m | sg |
| 48 | C- -------- | 49 | conj | ||
| 49 | V- 3AAI-S-- | 50 | verb | sg |
Adding Person ps¶
In [389]:
def person(row):
if re.search('.*1.*', str(row)):
return 'p1'
if re.search('.*2.*', str(row)):
return 'p2'
if re.search('.*3.*', str(row)):
return 'p3'
else:
return ''
In [390]:
featureprep['ps']=featureprep['morphology'].apply(person)
featureprep.head(20)
Out[390]:
| morphology | orig_order | sp | gn | nu | ps | |
|---|---|---|---|---|---|---|
| 0 | N- ----NSF- | 1 | noun | f | sg | |
| 1 | N- ----GSF- | 2 | noun | f | sg | |
| 2 | N- ----GSM- | 3 | noun | m | sg | |
| 3 | N- ----GSM- | 4 | noun | m | sg | |
| 4 | N- ----GSM- | 5 | noun | m | sg | |
| 5 | N- ----GSM- | 6 | noun | m | sg | |
| 6 | N- ----GSM- | 7 | noun | m | sg | |
| 7 | N- ----GSM- | 8 | noun | m | sg | |
| 8 | N- ----NSM- | 9 | noun | m | sg | |
| 9 | V- 3AAI-S-- | 10 | verb | sg | p3 | |
| 10 | RA ----ASM- | 11 | art-def | m | sg | |
| 11 | N- ----ASM- | 12 | noun | m | sg | |
| 12 | N- ----NSM- | 13 | noun | m | sg | |
| 13 | C- -------- | 14 | conj | |||
| 14 | V- 3AAI-S-- | 15 | verb | sg | p3 | |
| 15 | RA ----ASM- | 16 | art-def | m | sg | |
| 16 | N- ----ASM- | 17 | noun | m | sg | |
| 17 | N- ----NSM- | 18 | noun | m | sg | |
| 18 | C- -------- | 19 | conj | |||
| 19 | V- 3AAI-S-- | 20 | verb | sg | p3 |
Adding Case case¶
In [391]:
def case(row):
if re.search('......N...$', str(row)):
return 'nominative'
if re.search('......G...$', str(row)):
return 'genitive'
if re.search('......D...$', str(row)):
return 'dative'
if re.search('......A...$', str(row)):
return 'accusative'
else:
return ''
In [392]:
featureprep['case']=featureprep['morphology'].apply(case)
featureprep.head(20)
Out[392]:
| morphology | orig_order | sp | gn | nu | ps | case | |
|---|---|---|---|---|---|---|---|
| 0 | N- ----NSF- | 1 | noun | f | sg | nominative | |
| 1 | N- ----GSF- | 2 | noun | f | sg | genitive | |
| 2 | N- ----GSM- | 3 | noun | m | sg | genitive | |
| 3 | N- ----GSM- | 4 | noun | m | sg | genitive | |
| 4 | N- ----GSM- | 5 | noun | m | sg | genitive | |
| 5 | N- ----GSM- | 6 | noun | m | sg | genitive | |
| 6 | N- ----GSM- | 7 | noun | m | sg | genitive | |
| 7 | N- ----GSM- | 8 | noun | m | sg | genitive | |
| 8 | N- ----NSM- | 9 | noun | m | sg | nominative | |
| 9 | V- 3AAI-S-- | 10 | verb | sg | p3 | ||
| 10 | RA ----ASM- | 11 | art-def | m | sg | accusative | |
| 11 | N- ----ASM- | 12 | noun | m | sg | accusative | |
| 12 | N- ----NSM- | 13 | noun | m | sg | nominative | |
| 13 | C- -------- | 14 | conj | ||||
| 14 | V- 3AAI-S-- | 15 | verb | sg | p3 | ||
| 15 | RA ----ASM- | 16 | art-def | m | sg | accusative | |
| 16 | N- ----ASM- | 17 | noun | m | sg | accusative | |
| 17 | N- ----NSM- | 18 | noun | m | sg | nominative | |
| 18 | C- -------- | 19 | conj | ||||
| 19 | V- 3AAI-S-- | 20 | verb | sg | p3 |
Adding Tense vt¶
In [393]:
def tense(row):
if re.search('...A.*', str(row)):
return 'aorist'
if re.search('...P.*', str(row)):
return 'present'
if re.search('...F.*', str(row)):
return 'future'
if re.search('...I.*', str(row)):
return 'imperfect'
if re.search('...Y.*', str(row)):
return 'plusquamperfect'
if re.search('...X.*', str(row)):
return 'perfect'
else:
return ''
In [394]:
featureprep['vt']=featureprep['morphology'].apply(tense)
featureprep.head(20)
Out[394]:
| morphology | orig_order | sp | gn | nu | ps | case | vt | |
|---|---|---|---|---|---|---|---|---|
| 0 | N- ----NSF- | 1 | noun | f | sg | nominative | future | |
| 1 | N- ----GSF- | 2 | noun | f | sg | genitive | future | |
| 2 | N- ----GSM- | 3 | noun | m | sg | genitive | ||
| 3 | N- ----GSM- | 4 | noun | m | sg | genitive | ||
| 4 | N- ----GSM- | 5 | noun | m | sg | genitive | ||
| 5 | N- ----GSM- | 6 | noun | m | sg | genitive | ||
| 6 | N- ----GSM- | 7 | noun | m | sg | genitive | ||
| 7 | N- ----GSM- | 8 | noun | m | sg | genitive | ||
| 8 | N- ----NSM- | 9 | noun | m | sg | nominative | ||
| 9 | V- 3AAI-S-- | 10 | verb | sg | p3 | aorist | ||
| 10 | RA ----ASM- | 11 | art-def | m | sg | accusative | aorist | |
| 11 | N- ----ASM- | 12 | noun | m | sg | accusative | aorist | |
| 12 | N- ----NSM- | 13 | noun | m | sg | nominative | ||
| 13 | C- -------- | 14 | conj | |||||
| 14 | V- 3AAI-S-- | 15 | verb | sg | p3 | aorist | ||
| 15 | RA ----ASM- | 16 | art-def | m | sg | accusative | aorist | |
| 16 | N- ----ASM- | 17 | noun | m | sg | accusative | aorist | |
| 17 | N- ----NSM- | 18 | noun | m | sg | nominative | ||
| 18 | C- -------- | 19 | conj | |||||
| 19 | V- 3AAI-S-- | 20 | verb | sg | p3 | aorist |
Adding Voice voice¶
In [395]:
def voice(row):
if re.search('....A.....$', str(row)):
return 'active'
if re.search('....M.....$', str(row)):
return 'middle'
if re.search('....P.....$', str(row)):
return 'passive'
else:
return ''
In [396]:
featureprep['voice']=featureprep['morphology'].apply(voice)
featureprep.head(20)
Out[396]:
| morphology | orig_order | sp | gn | nu | ps | case | vt | voice | |
|---|---|---|---|---|---|---|---|---|---|
| 0 | N- ----NSF- | 1 | noun | f | sg | nominative | future | ||
| 1 | N- ----GSF- | 2 | noun | f | sg | genitive | future | ||
| 2 | N- ----GSM- | 3 | noun | m | sg | genitive | |||
| 3 | N- ----GSM- | 4 | noun | m | sg | genitive | |||
| 4 | N- ----GSM- | 5 | noun | m | sg | genitive | |||
| 5 | N- ----GSM- | 6 | noun | m | sg | genitive | |||
| 6 | N- ----GSM- | 7 | noun | m | sg | genitive | |||
| 7 | N- ----GSM- | 8 | noun | m | sg | genitive | |||
| 8 | N- ----NSM- | 9 | noun | m | sg | nominative | |||
| 9 | V- 3AAI-S-- | 10 | verb | sg | p3 | aorist | active | ||
| 10 | RA ----ASM- | 11 | art-def | m | sg | accusative | aorist | ||
| 11 | N- ----ASM- | 12 | noun | m | sg | accusative | aorist | ||
| 12 | N- ----NSM- | 13 | noun | m | sg | nominative | |||
| 13 | C- -------- | 14 | conj | ||||||
| 14 | V- 3AAI-S-- | 15 | verb | sg | p3 | aorist | active | ||
| 15 | RA ----ASM- | 16 | art-def | m | sg | accusative | aorist | ||
| 16 | N- ----ASM- | 17 | noun | m | sg | accusative | aorist | ||
| 17 | N- ----NSM- | 18 | noun | m | sg | nominative | |||
| 18 | C- -------- | 19 | conj | ||||||
| 19 | V- 3AAI-S-- | 20 | verb | sg | p3 | aorist | active |
Adding Mood mood¶
In [397]:
def mood(row):
if re.search('.....I....$', str(row)):
return 'indicative'
if re.search('.....D....$', str(row)):
return 'imperative'
if re.search('.....N....$', str(row)):
return 'infinitive'
if re.search('.....O....$', str(row)):
return 'optative'
if re.search('.....P....$', str(row)):
return 'participle'
if re.search('.....S....$', str(row)):
return 'subjunctive'
else:
return ''
In [398]:
featureprep['mood']=featureprep['morphology'].apply(mood)
featureprep.head(50)
Out[398]:
| morphology | orig_order | sp | gn | nu | ps | case | vt | voice | mood | |
|---|---|---|---|---|---|---|---|---|---|---|
| 0 | N- ----NSF- | 1 | noun | f | sg | nominative | future | |||
| 1 | N- ----GSF- | 2 | noun | f | sg | genitive | future | |||
| 2 | N- ----GSM- | 3 | noun | m | sg | genitive | ||||
| 3 | N- ----GSM- | 4 | noun | m | sg | genitive | ||||
| 4 | N- ----GSM- | 5 | noun | m | sg | genitive | ||||
| 5 | N- ----GSM- | 6 | noun | m | sg | genitive | ||||
| 6 | N- ----GSM- | 7 | noun | m | sg | genitive | ||||
| 7 | N- ----GSM- | 8 | noun | m | sg | genitive | ||||
| 8 | N- ----NSM- | 9 | noun | m | sg | nominative | ||||
| 9 | V- 3AAI-S-- | 10 | verb | sg | p3 | aorist | active | indicative | ||
| 10 | RA ----ASM- | 11 | art-def | m | sg | accusative | aorist | |||
| 11 | N- ----ASM- | 12 | noun | m | sg | accusative | aorist | |||
| 12 | N- ----NSM- | 13 | noun | m | sg | nominative | ||||
| 13 | C- -------- | 14 | conj | |||||||
| 14 | V- 3AAI-S-- | 15 | verb | sg | p3 | aorist | active | indicative | ||
| 15 | RA ----ASM- | 16 | art-def | m | sg | accusative | aorist | |||
| 16 | N- ----ASM- | 17 | noun | m | sg | accusative | aorist | |||
| 17 | N- ----NSM- | 18 | noun | m | sg | nominative | ||||
| 18 | C- -------- | 19 | conj | |||||||
| 19 | V- 3AAI-S-- | 20 | verb | sg | p3 | aorist | active | indicative | ||
| 20 | RA ----ASM- | 21 | art-def | m | sg | accusative | aorist | |||
| 21 | N- ----ASM- | 22 | noun | m | sg | accusative | aorist | |||
| 22 | C- -------- | 23 | conj | |||||||
| 23 | RA ----APM- | 24 | art-def | m | pl | accusative | aorist | |||
| 24 | N- ----APM- | 25 | noun | m | pl | accusative | aorist | |||
| 25 | RP ----GSM- | 26 | pron-prs | m | sg | genitive | ||||
| 26 | N- ----NSM- | 27 | noun | m | sg | nominative | ||||
| 27 | C- -------- | 28 | conj | |||||||
| 28 | V- 3AAI-S-- | 29 | verb | sg | p3 | aorist | active | indicative | ||
| 29 | RA ----ASM- | 30 | art-def | m | sg | accusative | aorist | |||
| 30 | N- ----ASM- | 31 | noun | m | sg | accusative | aorist | |||
| 31 | C- -------- | 32 | conj | |||||||
| 32 | RA ----ASM- | 33 | art-def | m | sg | accusative | aorist | |||
| 33 | N- ----ASM- | 34 | noun | m | sg | accusative | aorist | |||
| 34 | P- -------- | 35 | prep | |||||||
| 35 | RA ----GSF- | 36 | art-def | f | sg | genitive | future | |||
| 36 | N- ----GSF- | 37 | noun | f | sg | genitive | future | |||
| 37 | N- ----NSM- | 38 | noun | m | sg | nominative | ||||
| 38 | C- -------- | 39 | conj | |||||||
| 39 | V- 3AAI-S-- | 40 | verb | sg | p3 | aorist | active | indicative | ||
| 40 | RA ----ASM- | 41 | art-def | m | sg | accusative | aorist | |||
| 41 | N- ----ASM- | 42 | noun | m | sg | accusative | aorist | |||
| 42 | N- ----NSM- | 43 | noun | m | sg | nominative | ||||
| 43 | C- -------- | 44 | conj | |||||||
| 44 | V- 3AAI-S-- | 45 | verb | sg | p3 | aorist | active | indicative | ||
| 45 | RA ----ASM- | 46 | art-def | m | sg | accusative | aorist | |||
| 46 | N- ----ASM- | 47 | noun | m | sg | accusative | aorist | |||
| 47 | N- ----NSM- | 48 | noun | m | sg | nominative | ||||
| 48 | C- -------- | 49 | conj | |||||||
| 49 | V- 3AAI-S-- | 50 | verb | sg | p3 | aorist | active | indicative |
Adding degree degree¶
In [399]:
def degree(row):
if re.search('C$', str(row)):
return 'comparative'
if re.search('S$', str(row)):
return 'superlative'
else:
return ''
In [400]:
featureprep['degree']=featureprep['morphology'].apply(propernoun)
featureprep.head(50)
Out[400]:
| morphology | orig_order | sp | gn | nu | ps | case | vt | voice | mood | degree | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | N- ----NSF- | 1 | noun | f | sg | nominative | future | ||||
| 1 | N- ----GSF- | 2 | noun | f | sg | genitive | future | ||||
| 2 | N- ----GSM- | 3 | noun | m | sg | genitive | |||||
| 3 | N- ----GSM- | 4 | noun | m | sg | genitive | |||||
| 4 | N- ----GSM- | 5 | noun | m | sg | genitive | |||||
| 5 | N- ----GSM- | 6 | noun | m | sg | genitive | |||||
| 6 | N- ----GSM- | 7 | noun | m | sg | genitive | |||||
| 7 | N- ----GSM- | 8 | noun | m | sg | genitive | |||||
| 8 | N- ----NSM- | 9 | noun | m | sg | nominative | |||||
| 9 | V- 3AAI-S-- | 10 | verb | sg | p3 | aorist | active | indicative | |||
| 10 | RA ----ASM- | 11 | art-def | m | sg | accusative | aorist | ||||
| 11 | N- ----ASM- | 12 | noun | m | sg | accusative | aorist | ||||
| 12 | N- ----NSM- | 13 | noun | m | sg | nominative | |||||
| 13 | C- -------- | 14 | conj | ||||||||
| 14 | V- 3AAI-S-- | 15 | verb | sg | p3 | aorist | active | indicative | |||
| 15 | RA ----ASM- | 16 | art-def | m | sg | accusative | aorist | ||||
| 16 | N- ----ASM- | 17 | noun | m | sg | accusative | aorist | ||||
| 17 | N- ----NSM- | 18 | noun | m | sg | nominative | |||||
| 18 | C- -------- | 19 | conj | ||||||||
| 19 | V- 3AAI-S-- | 20 | verb | sg | p3 | aorist | active | indicative | |||
| 20 | RA ----ASM- | 21 | art-def | m | sg | accusative | aorist | ||||
| 21 | N- ----ASM- | 22 | noun | m | sg | accusative | aorist | ||||
| 22 | C- -------- | 23 | conj | ||||||||
| 23 | RA ----APM- | 24 | art-def | m | pl | accusative | aorist | ||||
| 24 | N- ----APM- | 25 | noun | m | pl | accusative | aorist | ||||
| 25 | RP ----GSM- | 26 | pron-prs | m | sg | genitive | |||||
| 26 | N- ----NSM- | 27 | noun | m | sg | nominative | |||||
| 27 | C- -------- | 28 | conj | ||||||||
| 28 | V- 3AAI-S-- | 29 | verb | sg | p3 | aorist | active | indicative | |||
| 29 | RA ----ASM- | 30 | art-def | m | sg | accusative | aorist | ||||
| 30 | N- ----ASM- | 31 | noun | m | sg | accusative | aorist | ||||
| 31 | C- -------- | 32 | conj | ||||||||
| 32 | RA ----ASM- | 33 | art-def | m | sg | accusative | aorist | ||||
| 33 | N- ----ASM- | 34 | noun | m | sg | accusative | aorist | ||||
| 34 | P- -------- | 35 | prep | ||||||||
| 35 | RA ----GSF- | 36 | art-def | f | sg | genitive | future | ||||
| 36 | N- ----GSF- | 37 | noun | f | sg | genitive | future | ||||
| 37 | N- ----NSM- | 38 | noun | m | sg | nominative | |||||
| 38 | C- -------- | 39 | conj | ||||||||
| 39 | V- 3AAI-S-- | 40 | verb | sg | p3 | aorist | active | indicative | |||
| 40 | RA ----ASM- | 41 | art-def | m | sg | accusative | aorist | ||||
| 41 | N- ----ASM- | 42 | noun | m | sg | accusative | aorist | ||||
| 42 | N- ----NSM- | 43 | noun | m | sg | nominative | |||||
| 43 | C- -------- | 44 | conj | ||||||||
| 44 | V- 3AAI-S-- | 45 | verb | sg | p3 | aorist | active | indicative | |||
| 45 | RA ----ASM- | 46 | art-def | m | sg | accusative | aorist | ||||
| 46 | N- ----ASM- | 47 | noun | m | sg | accusative | aorist | ||||
| 47 | N- ----NSM- | 48 | noun | m | sg | nominative | |||||
| 48 | C- -------- | 49 | conj | ||||||||
| 49 | V- 3AAI-S-- | 50 | verb | sg | p3 | aorist | active | indicative |
Exporting proces¶
In [401]:
featureprep.head()
Out[401]:
| morphology | orig_order | sp | gn | nu | ps | case | vt | voice | mood | degree | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | N- ----NSF- | 1 | noun | f | sg | nominative | future | ||||
| 1 | N- ----GSF- | 2 | noun | f | sg | genitive | future | ||||
| 2 | N- ----GSM- | 3 | noun | m | sg | genitive | |||||
| 3 | N- ----GSM- | 4 | noun | m | sg | genitive | |||||
| 4 | N- ----GSM- | 5 | noun | m | sg | genitive |
reorder¶
sorting first...
In [402]:
featureprep.sort_values(['orig_order'], ascending=True).head(10)
Out[402]:
| morphology | orig_order | sp | gn | nu | ps | case | vt | voice | mood | degree | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | N- ----NSF- | 1 | noun | f | sg | nominative | future | ||||
| 1 | N- ----GSF- | 2 | noun | f | sg | genitive | future | ||||
| 2 | N- ----GSM- | 3 | noun | m | sg | genitive | |||||
| 3 | N- ----GSM- | 4 | noun | m | sg | genitive | |||||
| 4 | N- ----GSM- | 5 | noun | m | sg | genitive | |||||
| 5 | N- ----GSM- | 6 | noun | m | sg | genitive | |||||
| 6 | N- ----GSM- | 7 | noun | m | sg | genitive | |||||
| 7 | N- ----GSM- | 8 | noun | m | sg | genitive | |||||
| 8 | N- ----NSM- | 9 | noun | m | sg | nominative | |||||
| 9 | V- 3AAI-S-- | 10 | verb | sg | p3 | aorist | active | indicative |
to excel spreadsheet...¶
In [403]:
featureprep.to_excel('d:/OneDrive/1200_AUS-research/Fabric-TEXT/0_data_SBLGNT/feature-dev/morphology.xlsx', index=None)
In [404]:
# export single features into tf files
featureprep['sp'].to_csv('d:/OneDrive/1200_AUS-research/Fabric-TEXT/0_data_SBLGNT/feature-dev/sp2.tf', index=None)
featureprep['gn'].to_csv('d:/OneDrive/1200_AUS-research/Fabric-TEXT/0_data_SBLGNT/feature-dev/gn2.tf', index=None)
featureprep['nu'].to_csv('d:/OneDrive/1200_AUS-research/Fabric-TEXT/0_data_SBLGNT/feature-dev/nu.tf', index=None)
featureprep['ps'].to_csv('d:/OneDrive/1200_AUS-research/Fabric-TEXT/0_data_SBLGNT/feature-dev/ps2.tf', index=None)
featureprep['case'].to_csv('d:/OneDrive/1200_AUS-research/Fabric-TEXT/0_data_SBLGNT/feature-dev/case.tf', index=None)
featureprep['vt'].to_csv('d:/OneDrive/1200_AUS-research/Fabric-TEXT/0_data_SBLGNT/feature-dev/vt.tf', index=None)
featureprep['voice'].to_csv('d:/OneDrive/1200_AUS-research/Fabric-TEXT/0_data_SBLGNT/feature-dev/voice.tf', index=None)
featureprep['mood'].to_csv('d:/OneDrive/1200_AUS-research/Fabric-TEXT/0_data_SBLGNT/feature-dev/mood.tf', index=None)
featureprep['degree'].to_csv('d:/OneDrive/1200_AUS-research/Fabric-TEXT/0_data_SBLGNT/feature-dev/degree.tf', index=None)
Translitaration¶
In [4]:
translitadd=pd.read_csv('d:/OneDrive/1200_AUS-research/Fabric-TEXT/0_data_SBLGNT/feature-dev/lemma_copy.tf',header=None, delimiter='\t',encoding='utf-8')
pd.set_option('display.max_columns', 50)
translitadd.head(10)
Out[4]:
| 0 | |
|---|---|
| 0 | βίβλος |
| 1 | γένεσις |
| 2 | Ἰησοῦς |
| 3 | Χριστός |
| 4 | υἱός |
| 5 | Δαυίδ |
| 6 | υἱός |
| 7 | Ἀβραάμ |
| 8 | Ἀβραάμ |
| 9 | γεννάω |
In [5]:
translitadd['lemma']=translitadd[0]
translitadd.head(5)
Out[5]:
| 0 | lemma | |
|---|---|---|
| 0 | βίβλος | βίβλος |
| 1 | γένεσις | γένεσις |
| 2 | Ἰησοῦς | Ἰησοῦς |
| 3 | Χριστός | Χριστός |
| 4 | υἱός | υἱός |
In [6]:
translitadd=translitadd[['lemma']]
translitadd.head(5)
Out[6]:
| lemma | |
|---|---|
| 0 | βίβλος |
| 1 | γένεσις |
| 2 | Ἰησοῦς |
| 3 | Χριστός |
| 4 | υἱός |
In [7]:
translitadd['orig_order'] = translitadd.index +1
translitadd.head(5)
Out[7]:
| lemma | orig_order | |
|---|---|---|
| 0 | βίβλος | 1 |
| 1 | γένεσις | 2 |
| 2 | Ἰησοῦς | 3 |
| 3 | Χριστός | 4 |
| 4 | υἱός | 5 |
In [9]:
from unidecode import unidecode
In [10]:
s = "βίβλος"
s = unidecode(s)
print(s)
biblos
In [11]:
translitadd['translit'] = translitadd['lemma'].apply(unidecode)
In [12]:
translitadd.head(5)
Out[12]:
| lemma | orig_order | translit | |
|---|---|---|---|
| 0 | βίβλος | 1 | biblos |
| 1 | γένεσις | 2 | genesis |
| 2 | Ἰησοῦς | 3 | Iesous |
| 3 | Χριστός | 4 | Khristos |
| 4 | υἱός | 5 | uios |
In [413]:
translitadd['translit'].to_csv('d:/OneDrive/1200_AUS-research/Fabric-TEXT/0_data_SBLGNT/feature-dev/lemma_translit.tf', index=None)
ABC dictionary order¶
In [414]:
ABC1=pd.read_csv('d:/OneDrive/1200_AUS-research/Fabric-TEXT/0_data_SBLGNT/feature-dev/lemma_copy.tf',header=None, delimiter='\t',encoding='utf-8')
pd.set_option('display.max_columns', 50)
ABC1.head(10)
Out[414]:
| 0 | |
|---|---|
| 0 | βίβλος |
| 1 | γένεσις |
| 2 | Ἰησοῦς |
| 3 | Χριστός |
| 4 | υἱός |
| 5 | Δαυίδ |
| 6 | υἱός |
| 7 | Ἀβραάμ |
| 8 | Ἀβραάμ |
| 9 | γεννάω |
In [415]:
ABC1['lemma']=lemma[0]
ABC1.head(5)
Out[415]:
| 0 | lemma | |
|---|---|---|
| 0 | βίβλος | βίβλος |
| 1 | γένεσις | γένεσις |
| 2 | Ἰησοῦς | Ἰησοῦς |
| 3 | Χριστός | Χριστός |
| 4 | υἱός | υἱός |
In [416]:
ABC1['orig_order'] = ABC1.index +1
ABC1.head(5)
Out[416]:
| 0 | lemma | orig_order | |
|---|---|---|---|
| 0 | βίβλος | βίβλος | 1 |
| 1 | γένεσις | γένεσις | 2 |
| 2 | Ἰησοῦς | Ἰησοῦς | 3 |
| 3 | Χριστός | Χριστός | 4 |
| 4 | υἱός | υἱός | 5 |
In [417]:
ABC1=ABC1[['orig_order','lemma']]
ABC1.head(5)
Out[417]:
| orig_order | lemma | |
|---|---|---|
| 0 | 1 | βίβλος |
| 1 | 2 | γένεσις |
| 2 | 3 | Ἰησοῦς |
| 3 | 4 | Χριστός |
| 4 | 5 | υἱός |
In [418]:
ABC1.describe()
Out[418]:
| orig_order | |
|---|---|
| count | 137554.000000 |
| mean | 68777.500000 |
| std | 39708.563801 |
| min | 1.000000 |
| 25% | 34389.250000 |
| 50% | 68777.500000 |
| 75% | 103165.750000 |
| max | 137554.000000 |
In [419]:
ABCdict = ABC1.drop_duplicates(['lemma']).sort_values(by='lemma', ascending=[True])
ABCdict.head(10)
Out[419]:
| orig_order | lemma | |
|---|---|---|
| 68479 | 68480 | Αἰγύπτιος |
| 69633 | 69634 | Αἰθίοψ |
| 70464 | 70465 | Αἰνέας |
| 50739 | 50740 | Αἰνών |
| 679 | 680 | Αἴγυπτος |
| 30811 | 30812 | Αὐγοῦστος |
| 87589 | 87590 | Βάαλ |
| 75520 | 75521 | Βέροια |
| 171 | 172 | Βαβυλών |
| 128506 | 128507 | Βαλάκ |
In [420]:
ABCdict.describe()
Out[420]:
| orig_order | |
|---|---|
| count | 5461.000000 |
| mean | 55051.110969 |
| std | 42441.209940 |
| min | 1.000000 |
| 25% | 12643.000000 |
| 50% | 48785.000000 |
| 75% | 90141.000000 |
| max | 137334.000000 |
In [421]:
ABC1.to_excel('d:/OneDrive/1200_AUS-research/Fabric-TEXT/0_data_SBLGNT/feature-dev/ABC1order.xlsx', encoding='utf-8')
Now I am ordering the word alphabetically iwth libreoffice writer since I cannot do that in pandas (yet?).
In [422]:
ABC2=pd.read_excel('d:/OneDrive/1200_AUS-research/Fabric-TEXT/0_data_SBLGNT/feature-dev/ABC2order.xlsx')
pd.set_option('display.max_columns', 50)
ABC2.head(10)
Out[422]:
| Unnamed: 0 | orig_order | lemma | ABC order | |
|---|---|---|---|---|
| 0 | 29678 | 29679 | Ἀαρών | 1 |
| 1 | 131340 | 131341 | Ἀβαδδών | 2 |
| 2 | 100253 | 100254 | ἀβαρής | 3 |
| 3 | 28001 | 28002 | αββα | 4 |
| 4 | 14094 | 14095 | Ἅβελ | 5 |
| 5 | 108 | 109 | Ἀβιά | 6 |
| 6 | 19523 | 19524 | Ἀβιαθάρ | 7 |
| 7 | 31682 | 31683 | Ἀβιληνή | 8 |
| 8 | 190 | 191 | Ἀβιούδ | 9 |
| 9 | 7 | 8 | Ἀβραάμ | 10 |
Now we merge the ABCorder dataframe with the original lemma DF.
In [423]:
lemma_ABC=pd.merge (ABC1, ABC2,
on='lemma',
how='outer')
lemma_ABC.head(5)
Out[423]:
| orig_order_x | lemma | Unnamed: 0 | orig_order_y | ABC order | |
|---|---|---|---|---|---|
| 0 | 1 | βίβλος | 0 | 1 | 970 |
| 1 | 26440 | βίβλος | 0 | 1 | 970 |
| 2 | 31717 | βίβλος | 0 | 1 | 970 |
| 3 | 45660 | βίβλος | 0 | 1 | 970 |
| 4 | 64886 | βίβλος | 0 | 1 | 970 |
In [424]:
lemma_ABC.describe()
Out[424]:
| orig_order_x | Unnamed: 0 | orig_order_y | ABC order | |
|---|---|---|---|---|
| count | 137554.000000 | 137554.000000 | 137554.000000 | 137554.00000 |
| mean | 68777.500000 | 7050.531566 | 7051.531566 | 2676.19798 |
| std | 39708.563801 | 20152.248998 | 20152.248998 | 1339.74175 |
| min | 1.000000 | 0.000000 | 1.000000 | 1.00000 |
| 25% | 34389.250000 | 25.000000 | 26.000000 | 1501.00000 |
| 50% | 68777.500000 | 400.000000 | 401.000000 | 2727.00000 |
| 75% | 103165.750000 | 2097.250000 | 2098.250000 | 3598.00000 |
| max | 137554.000000 | 137333.000000 | 137334.000000 | 5461.00000 |
In [425]:
lemma_ABC.sort_values(['orig_order_x'], ascending=True).head(10)
Out[425]:
| orig_order_x | lemma | Unnamed: 0 | orig_order_y | ABC order | |
|---|---|---|---|---|---|
| 0 | 1 | βίβλος | 0 | 1 | 970 |
| 10 | 2 | γένεσις | 1 | 2 | 1074 |
| 15 | 3 | Ἰησοῦς | 2 | 3 | 2406 |
| 921 | 4 | Χριστός | 3 | 4 | 5385 |
| 1449 | 5 | υἱός | 4 | 5 | 5053 |
| 1824 | 6 | Δαυίδ | 5 | 6 | 1156 |
| 1450 | 7 | υἱός | 4 | 5 | 5053 |
| 1883 | 8 | Ἀβραάμ | 7 | 8 | 10 |
| 1884 | 9 | Ἀβραάμ | 7 | 8 | 10 |
| 1956 | 10 | γεννάω | 9 | 10 | 1077 |
In [426]:
lemma_ABC.to_excel('d:/OneDrive/1200_AUS-research/Fabric-TEXT/0_data_SBLGNT/feature-dev/lemma_abc.xlsx')
Word Frequency¶
In [427]:
frequencyadd=pd.read_csv('d:/OneDrive/1200_AUS-research/Fabric-TEXT/0_data_SBLGNT/feature-dev/lemma_copy.tf',header=None, delimiter='\t',encoding='utf-8')
pd.set_option('display.max_columns', 50)
frequencyadd.head(20)
Out[427]:
| 0 | |
|---|---|
| 0 | βίβλος |
| 1 | γένεσις |
| 2 | Ἰησοῦς |
| 3 | Χριστός |
| 4 | υἱός |
| 5 | Δαυίδ |
| 6 | υἱός |
| 7 | Ἀβραάμ |
| 8 | Ἀβραάμ |
| 9 | γεννάω |
| 10 | ὁ |
| 11 | Ἰσαάκ |
| 12 | Ἰσαάκ |
| 13 | δέ |
| 14 | γεννάω |
| 15 | ὁ |
| 16 | Ἰακώβ |
| 17 | Ἰακώβ |
| 18 | δέ |
| 19 | γεννάω |
In [428]:
frequencyadd['orig_order'] = frequencyadd.index +1
frequencyadd['lemma']=frequencyadd[0]
frequencyadd=frequencyadd[['orig_order','lemma']]
frequencyadd.head(5)
Out[428]:
| orig_order | lemma | |
|---|---|---|
| 0 | 1 | βίβλος |
| 1 | 2 | γένεσις |
| 2 | 3 | Ἰησοῦς |
| 3 | 4 | Χριστός |
| 4 | 5 | υἱός |
In [429]:
frequencyadd["freq_lemma"]=frequencyadd.groupby(["lemma"])["lemma"].transform("count")
#("count") is actually utilizing the 'count' function!
frequencyadd.head(20)
Out[429]:
| orig_order | lemma | freq_lemma | |
|---|---|---|---|
| 0 | 1 | βίβλος | 10 |
| 1 | 2 | γένεσις | 5 |
| 2 | 3 | Ἰησοῦς | 906 |
| 3 | 4 | Χριστός | 528 |
| 4 | 5 | υἱός | 375 |
| 5 | 6 | Δαυίδ | 59 |
| 6 | 7 | υἱός | 375 |
| 7 | 8 | Ἀβραάμ | 73 |
| 8 | 9 | Ἀβραάμ | 73 |
| 9 | 10 | γεννάω | 97 |
| 10 | 11 | ὁ | 19769 |
| 11 | 12 | Ἰσαάκ | 20 |
| 12 | 13 | Ἰσαάκ | 20 |
| 13 | 14 | δέ | 2766 |
| 14 | 15 | γεννάω | 97 |
| 15 | 16 | ὁ | 19769 |
| 16 | 17 | Ἰακώβ | 27 |
| 17 | 18 | Ἰακώβ | 27 |
| 18 | 19 | δέ | 2766 |
| 19 | 20 | γεννάω | 97 |
In [430]:
frequencyadd.to_excel('d:/OneDrive/1200_AUS-research/Fabric-TEXT/0_data_SBLGNT/feature-dev/lemma_freq.xlsx')
English Dictionary¶
Lets first load the NA1904 BibleOL dictionary:
In [431]:
BOLgreekDICT=pd.read_excel('d:/OneDrive/1200_AUS-research/Fabric-TEXT/0_data_SBLGNT/feature-dev/NA1904_dictionary_v1.0.xlsx')
pd.set_option('display.max_columns', 50)
BOLgreekDICT.head(20)
Out[431]:
| orig abc order | Occurrences | Lexeme | Lexeme_dict | Strong's number | Strong's unreliable? | gloss | |
|---|---|---|---|---|---|---|---|
| 0 | 1 | 5 | Ἀαρών | Ἀαρών, ὁ | 2 | no | Aaron |
| 1 | 2 | 1 | Ἀβαδδών | Ἀβαδδών, ὁ | 3 | no | Abaddon |
| 2 | 3 | 1 | ἀβαρής | ἀβαρής, -ές | 4 | no | not burdensome |
| 3 | 4 | 3 | ἀββά | ἀββά, ὁ | 5 | no | Father |
| 4 | 5 | 4 | Ἅβελ | Ἅβελ, ὁ | 6 | no | Abel |
| 5 | 6 | 3 | Ἀβιά | Ἀβιά, ὁ | 7 | no | Abijah |
| 6 | 7 | 1 | Ἀβιαθάρ | Ἀβιαθάρ, ὁ | 8 | no | Abiathar |
| 7 | 8 | 1 | Ἀβιληνή | Ἀβιληνή, -ῆς, ἡ | 9 | no | Abilene |
| 8 | 9 | 2 | Ἀβιούδ | Ἀβιούδ, ὁ | 10 | no | Abiud |
| 9 | 10 | 73 | Ἀβραάμ | Ἀβραάμ, ὁ | 11 | no | Abraham |
| 10 | 11 | 9 | ἄβυσσος | ἄβυσσος, -ου, ἡ | 12 | no | abyss, unfathomable depth |
| 11 | 12 | 2 | Ἅγαβος | Ἅγαβος, -ου, ὁ | 13 | no | Agabus |
| 12 | 13 | 2 | ἀγαθοεργέω | ἀγαθοεργέω | 14 | no | perform good deeds |
| 13 | 14 | 9 | ἀγαθοποιέω | ἀγαθοποιέω | 15 | no | do that which is good |
| 14 | 15 | 1 | ἀγαθοποιΐα | ἀγαθοποιΐα, -ας, ἡ | 16 | no | doing of that which is good |
| 15 | 16 | 1 | ἀγαθοποιός | ἀγαθοποιός, -οῦ, ὁ | 17 | no | a doer of that which is good |
| 16 | 17 | 102 | ἀγαθός | ἀγαθός, -ή, -όν | 18 | no | good |
| 17 | 18 | 4 | ἀγαθωσύνη | ἀγαθωσύνη, -ης, ἡ | 19 | no | goodness |
| 18 | 19 | 5 | ἀγαλλίασις | ἀγαλλίασις, -εως, ἡ | 20 | no | exultation, exhilaration |
| 19 | 20 | 11 | ἀγαλλιάω | ἀγαλλιάω | 21 | no | exult, am full of joy |
In [432]:
BOLgreekDICT=BOLgreekDICT[['Lexeme','Lexeme_dict', 'Strong\'s number', 'gloss']]
BOLgreekDICT.head(10)
Out[432]:
| Lexeme | Lexeme_dict | Strong's number | gloss | |
|---|---|---|---|---|
| 0 | Ἀαρών | Ἀαρών, ὁ | 2 | Aaron |
| 1 | Ἀβαδδών | Ἀβαδδών, ὁ | 3 | Abaddon |
| 2 | ἀβαρής | ἀβαρής, -ές | 4 | not burdensome |
| 3 | ἀββά | ἀββά, ὁ | 5 | Father |
| 4 | Ἅβελ | Ἅβελ, ὁ | 6 | Abel |
| 5 | Ἀβιά | Ἀβιά, ὁ | 7 | Abijah |
| 6 | Ἀβιαθάρ | Ἀβιαθάρ, ὁ | 8 | Abiathar |
| 7 | Ἀβιληνή | Ἀβιληνή, -ῆς, ἡ | 9 | Abilene |
| 8 | Ἀβιούδ | Ἀβιούδ, ὁ | 10 | Abiud |
| 9 | Ἀβραάμ | Ἀβραάμ, ὁ | 11 | Abraham |
In [433]:
BOLgreekDICT.describe()
Out[433]:
| Strong's number | |
|---|---|
| count | 5433.000000 |
| mean | 2798.407878 |
| std | 1638.197697 |
| min | 1.000000 |
| 25% | 1370.000000 |
| 50% | 2754.000000 |
| 75% | 4237.000000 |
| max | 5624.000000 |
Lets load the SBLGNT lemmas
In [434]:
SBLGNTlemmas=pd.read_csv('d:/OneDrive/1200_AUS-research/Fabric-TEXT/0_data_SBLGNT/feature-dev/lemma_copy.tf',header=None, delimiter='\t',encoding='utf-8')
pd.set_option('display.max_columns', 50)
SBLGNTlemmas.head(2)
Out[434]:
| 0 | |
|---|---|
| 0 | βίβλος |
| 1 | γένεσις |
In [436]:
SBLGNTlemmas['orig_order']=SBLGNTlemmas.index +1
SBLGNTlemmas['Lexeme']=SBLGNTlemmas[0]
SBLGNTlemmas=SBLGNTlemmas[['orig_order','Lexeme']]
SBLGNTlemmas.head(5)
Out[436]:
| orig_order | Lexeme | |
|---|---|---|
| 0 | 1 | βίβλος |
| 1 | 2 | γένεσις |
| 2 | 3 | Ἰησοῦς |
| 3 | 4 | Χριστός |
| 4 | 5 | υἱός |
In [437]:
SBLGNTlemmas.describe()
Out[437]:
| orig_order | |
|---|---|
| count | 137554.000000 |
| mean | 68777.500000 |
| std | 39708.563801 |
| min | 1.000000 |
| 25% | 34389.250000 |
| 50% | 68777.500000 |
| 75% | 103165.750000 |
| max | 137554.000000 |
Now lets try a merge of the two files
In [438]:
SBLGNTglosses=pd.merge (SBLGNTlemmas,BOLgreekDICT,
on='Lexeme',
how='outer')
SBLGNTglosses.head(5)
Out[438]:
| orig_order | Lexeme | Lexeme_dict | Strong's number | gloss | |
|---|---|---|---|---|---|
| 0 | 1.0 | βίβλος | NaN | NaN | NaN |
| 1 | 26440.0 | βίβλος | NaN | NaN | NaN |
| 2 | 31717.0 | βίβλος | NaN | NaN | NaN |
| 3 | 45660.0 | βίβλος | NaN | NaN | NaN |
| 4 | 64886.0 | βίβλος | NaN | NaN | NaN |
In [439]:
SBLGNTglosses.describe()
Out[439]:
| orig_order | Strong's number | |
|---|---|---|
| count | 138318.000000 | 54761.000000 |
| mean | 68756.590379 | 3008.488377 |
| std | 39712.532678 | 1209.032138 |
| min | 1.000000 | 1.000000 |
| 25% | 34366.250000 | 2041.000000 |
| 50% | 68753.500000 | 3588.000000 |
| 75% | 103162.750000 | 3706.000000 |
| max | 137554.000000 | 5624.000000 |
In [440]:
SBLGNTglosses.head(20)
Out[440]:
| orig_order | Lexeme | Lexeme_dict | Strong's number | gloss | |
|---|---|---|---|---|---|
| 0 | 1.0 | βίβλος | NaN | NaN | NaN |
| 1 | 26440.0 | βίβλος | NaN | NaN | NaN |
| 2 | 31717.0 | βίβλος | NaN | NaN | NaN |
| 3 | 45660.0 | βίβλος | NaN | NaN | NaN |
| 4 | 64886.0 | βίβλος | NaN | NaN | NaN |
| 5 | 68873.0 | βίβλος | NaN | NaN | NaN |
| 6 | 76865.0 | βίβλος | NaN | NaN | NaN |
| 7 | 107214.0 | βίβλος | NaN | NaN | NaN |
| 8 | 128928.0 | βίβλος | NaN | NaN | NaN |
| 9 | 136490.0 | βίβλος | NaN | NaN | NaN |
| 10 | 2.0 | γένεσις | NaN | NaN | NaN |
| 11 | 281.0 | γένεσις | NaN | NaN | NaN |
| 12 | 29821.0 | γένεσις | NaN | NaN | NaN |
| 13 | 120472.0 | γένεσις | NaN | NaN | NaN |
| 14 | 121080.0 | γένεσις | NaN | NaN | NaN |
| 15 | 3.0 | Ἰησοῦς | Ἰησοῦς | 2424.0 | Jesus |
| 16 | 243.0 | Ἰησοῦς | Ἰησοῦς | 2424.0 | Jesus |
| 17 | 278.0 | Ἰησοῦς | Ἰησοῦς | 2424.0 | Jesus |
| 18 | 357.0 | Ἰησοῦς | Ἰησοῦς | 2424.0 | Jesus |
| 19 | 436.0 | Ἰησοῦς | Ἰησοῦς | 2424.0 | Jesus |
In [441]:
SBLGNTglosses.to_excel('d:/OneDrive/1200_AUS-research/Fabric-TEXT/0_data_SBLGNT/feature-dev/SBLGNTglosses.xlsx')
In [ ]:
Comparing with Tischendorf¶
In [442]:
TISCH = use('tisch', hoist=globals())
TF-app: C:\Users\Oliver Glanz/text-fabric-data/annotation/app-tisch/code
data: C:\Users\Oliver Glanz/text-fabric-data/codykingham/tischendorf_tf/tf/2.8
This is Text-Fabric 9.1.7 Api reference : https://annotation.github.io/text-fabric/tf/cheatsheet.html 31 features found and 0 ignored
Text-Fabric: Text-Fabric API 9.1.7, app-tisch v3, Search Reference
Data: TISCHENDORF_TF, Character table, Feature docs
Features:
Data: TISCHENDORF_TF, Character table, Feature docs
Features:
Tischendorf's 8th New Testament
str
the original morphology distinguished between two different interrogative pronouns, that distinction is found in originterrdiff
Text-Fabric API: names N F E L T S C TF directly usable
In [443]:
Tisch1 = '''
book book=Matthew book_code*
chapter chapter=1
word lex_og*
'''
Tisch1 = TISCH.search(Tisch1)
TISCH.show(Tisch1, start=1, end=1, condensed=True, colorMap={1:'pink'}, extraFeatures={'verse','vrsnum'})
0.22s 435 results
verse 1
Matthew 1:1
verse
verse=1
Βίβλος
lex_og=biblosvrsnum=1
γενέσεως
lex_og=genesisvrsnum=2
Ἰησοῦ
lex_og=Iesousvrsnum=3
Χριστοῦ
lex_og=Khristosvrsnum=4
υἱοῦ
lex_og=uiosvrsnum=5
Δαυεὶδ
lex_og=Dauidvrsnum=6
υἱοῦ
lex_og=uiosvrsnum=7
Ἀβραάμ.
lex_og=Abraamvrsnum=8
Testing SBLGNT¶
In [444]:
SBLGNT = use('SBLGNT/tf/6.12', hoist=globals())
This is Text-Fabric 9.1.7 Api reference : https://annotation.github.io/text-fabric/tf/cheatsheet.html 29 features found and 0 ignored
Text-Fabric: Text-Fabric API 9.1.7, no app configured
Data: D:\OneDrive\1200_AUS-research\Fabric-TEXT\0_data_SBLGNT\SBLGNT\tf/6.12
Features:
Data: D:\OneDrive\1200_AUS-research\Fabric-TEXT\0_data_SBLGNT\SBLGNT\tf/6.12
Features:
D:\OneDrive\1200_AUS-research\Fabric-TEXT\0_data_SBLGNT\SBLGNT\tf/6.12
book
str
A book name
book_code
str
Short book abbreviation
case
str
case
chapter
int
A chapter number
clause
str
A clause number
degree
str
degree
dict_abc_order
str
words with their dictionary order
gn
str
gender
lemma
str
Dictionary entry form
lemma_dictform
str
BibleOL Greek dictionary form
lemma_freq
str
word frequency
lemma_gloss
str
BibleOL English gloss
lemma_strongs
str
Strong's number
lemma_translit
str
lemma transliteration
mood
str
mood
morph
str
Word morphological tag based on Maurice A Robinson's analysis
normalized_word
str
Word in its normalized form
nu
str
number
orig_order
str
number of word position in NT
otype
str
ps
str
person
sentence
int
A sentence number
verse
int
A verse number
voice
str
voice
vrsnum
str
N-word in verse
vt
str
tense
word
str
Word with punctuation stripped
oslots
none
Text-Fabric API: names N F E L T S C TF directly usable
In [445]:
Search1 = '''
book book=Revelation book_code*
chapter chapter=1
sentence
clause
word lemma* morph* normalized_word* case* degree* gn* mood* nu* orig_order* ps* voice* vt* lemma_translit* dict_abc_order* lemma_freq* lemma_dictform* lemma_freq* lemma_gloss* lemma_strongs*
'''
Search1 = SBLGNT.search(Search1)
SBLGNT.show(Search1, start=1, end=1, condensed=True, colorMap={1:'pink'}, extraFeatures={'verse','vrsnum'})
2.64s 469 results
verse 1
Revelation 1:1
verse
verse=1
clause
ἀποκάλυψις
case=nominativedegree=dict_abc_order=597gn=flemma=ἀποκάλυψιςlemma_dictform=ἀποκάλυψις, -εως, ἡlemma_freq=17lemma_gloss=unveiling, uncovering, revealinglemma_strongs=602lemma_translit=apokalupsismood=morph=N- ----NSF-normalized_word=ἀποκάλυψιςnu=sgorig_order=127722ps=voice=vrsnum=1vt=future
Ἰησοῦ
case=genitivedegree=dict_abc_order=2406gn=mlemma=Ἰησοῦςlemma_dictform=Ἰησοῦςlemma_freq=906lemma_gloss=Jesuslemma_strongs=2424lemma_translit=Iesousmood=morph=N- ----GSM-normalized_word=Ἰησοῦnu=sgorig_order=127723ps=voice=vrsnum=2vt=
Χριστοῦ
case=genitivedegree=dict_abc_order=5385gn=mlemma=Χριστόςlemma_dictform=Χριστός, -οῦ, ὁlemma_freq=528lemma_gloss=anointed, Messiah, Christlemma_strongs=5547lemma_translit=Khristosmood=morph=N- ----GSM-normalized_word=Χριστοῦnu=sgorig_order=127724ps=voice=vrsnum=3vt=
clause
ἥν
case=accusativedegree=dict_abc_order=3609gn=flemma=ὅςlemma_dictform=ὅς, ἥ, ὅlemma_freq=1408lemma_gloss=who, which, what, thatlemma_strongs=3739lemma_translit=osmood=morph=RR ----ASF-normalized_word=ἥνnu=sgorig_order=127725ps=voice=vrsnum=4vt=aorist
ἔδωκε(ν)
case=degree=dict_abc_order=1320gn=lemma=δίδωμιlemma_dictform=δίδωμιlemma_freq=415lemma_gloss=offer, givelemma_strongs=1325lemma_translit=didomimood=indicativemorph=V- 3AAI-S--normalized_word=ἔδωκε(ν)nu=sgorig_order=127726ps=p3voice=activevrsnum=5vt=aorist
αὐτῷ
case=dativedegree=dict_abc_order=837gn=mlemma=αὐτόςlemma_dictform=αὐτός, -ή, -όlemma_freq=5546lemma_gloss=he, she, it, they, them, samelemma_strongs=846lemma_translit=autosmood=morph=RP ----DSM-normalized_word=αὐτῷnu=sgorig_order=127727ps=voice=vrsnum=6vt=
ὁ
case=nominativedegree=dict_abc_order=3455gn=mlemma=ὁlemma_dictform=ὁ, ἡ, τόlemma_freq=19769lemma_gloss=thelemma_strongs=3588lemma_translit=omood=morph=RA ----NSM-normalized_word=ὁnu=sgorig_order=127728ps=voice=vrsnum=7vt=
θεός
case=nominativedegree=dict_abc_order=2299gn=mlemma=θεόςlemma_dictform=θεός, -οῦ, ὁlemma_freq=1307lemma_gloss=God, godlemma_strongs=2316lemma_translit=theosmood=morph=N- ----NSM-normalized_word=θεόςnu=sgorig_order=127729ps=voice=vrsnum=8vt=
δεῖξαι
case=degree=dict_abc_order=1161gn=lemma=δείκνυμιlemma_dictform=δείκνυμιlemma_freq=33lemma_gloss=point out, showlemma_strongs=1166lemma_translit=deiknumimood=infinitivemorph=V- -AAN----normalized_word=δεῖξαιnu=orig_order=127730ps=voice=activevrsnum=9vt=aorist
τοῖς
case=dativedegree=dict_abc_order=3455gn=mlemma=ὁlemma_dictform=ὁ, ἡ, τόlemma_freq=19769lemma_gloss=thelemma_strongs=3588lemma_translit=omood=morph=RA ----DPM-normalized_word=τοῖςnu=plorig_order=127731ps=voice=vrsnum=10vt=present
δούλοις
case=dativedegree=dict_abc_order=1399gn=mlemma=δοῦλοςlemma_dictform=δούλη, -ης, ἡlemma_freq=126lemma_gloss=female slave, bonds-maidlemma_strongs=1399lemma_translit=doulosmood=morph=N- ----DPM-normalized_word=δούλοιςnu=plorig_order=127732ps=voice=vrsnum=11vt=present
αὐτοῦ
case=genitivedegree=dict_abc_order=837gn=mlemma=αὐτόςlemma_dictform=αὐτός, -ή, -όlemma_freq=5546lemma_gloss=he, she, it, they, them, samelemma_strongs=846lemma_translit=autosmood=morph=RP ----GSM-normalized_word=αὐτοῦnu=sgorig_order=127733ps=voice=vrsnum=12vt=
clause
ἅ
case=accusativedegree=dict_abc_order=3609gn=nlemma=ὅςlemma_dictform=ὅς, ἥ, ὅlemma_freq=1408lemma_gloss=who, which, what, thatlemma_strongs=3739lemma_translit=osmood=morph=RR ----APN-normalized_word=ἅnu=plorig_order=127734ps=voice=vrsnum=13vt=aorist
δεῖ
case=degree=dict_abc_order=1205gn=lemma=δέωlemma_dictform=δέομαιlemma_freq=154lemma_gloss=bindlemma_strongs=1210lemma_translit=deomood=indicativemorph=V- 3PAI-S--normalized_word=δεῖnu=sgorig_order=127735ps=p3voice=activevrsnum=14vt=aorist
γενέσθαι
case=degree=dict_abc_order=1092gn=lemma=γίνομαιlemma_dictform=γίνομαιlemma_freq=667lemma_gloss=come into being, am bornlemma_strongs=1096lemma_translit=ginomaimood=infinitivemorph=V- -AMN----normalized_word=γενέσθαιnu=orig_order=127736ps=voice=middlevrsnum=15vt=aorist
ἐν
case=degree=dict_abc_order=1711gn=lemma=ἐνlemma_dictform=ἐνlemma_freq=2733lemma_gloss=in, on, amonglemma_strongs=1722lemma_translit=enmood=morph=P- --------normalized_word=ἐνnu=orig_order=127737ps=voice=vrsnum=16vt=
τάχει
case=dativedegree=dict_abc_order=4887gn=nlemma=τάχοςlemma_dictform=τάχος, -ους, τόlemma_freq=8lemma_gloss=quickness, speedlemma_strongs=5034lemma_translit=takhosmood=morph=N- ----DSN-normalized_word=τάχειnu=sgorig_order=127738ps=voice=vrsnum=17vt=
clause
καί
case=degree=dict_abc_order=2515gn=lemma=καίlemma_dictform=καίlemma_freq=8973lemma_gloss=and, even, also, namelylemma_strongs=2532lemma_translit=kaimood=morph=C- --------normalized_word=καίnu=orig_order=127739ps=voice=vrsnum=18vt=
ἐσήμανε(ν)
case=degree=dict_abc_order=4448gn=lemma=σημαίνωlemma_dictform=σημαίνωlemma_freq=6lemma_gloss=signify, indicatelemma_strongs=4591lemma_translit=semainomood=indicativemorph=V- 3AAI-S--normalized_word=ἐσήμανε(ν)nu=sgorig_order=127740ps=p3voice=activevrsnum=19vt=aorist
ἀποστείλας
case=nominativedegree=dict_abc_order=641gn=mlemma=ἀποστέλλωlemma_dictform=ἀποστέλλωlemma_freq=132lemma_gloss=send forth, send, send away, dismisslemma_strongs=649lemma_translit=apostellomood=participlemorph=V- -AAPNSM-normalized_word=ἀποστείλαςnu=sgorig_order=127741ps=voice=activevrsnum=20vt=aorist
διά
case=degree=dict_abc_order=1217gn=lemma=διάlemma_dictform=διάlemma_freq=666lemma_gloss=through, on account oflemma_strongs=1223lemma_translit=diamood=morph=P- --------normalized_word=διάnu=orig_order=127742ps=voice=vrsnum=21vt=
τοῦ
case=genitivedegree=dict_abc_order=3455gn=mlemma=ὁlemma_dictform=ὁ, ἡ, τόlemma_freq=19769lemma_gloss=thelemma_strongs=3588lemma_translit=omood=morph=RA ----GSM-normalized_word=τοῦnu=sgorig_order=127743ps=voice=vrsnum=22vt=
ἀγγέλου
case=genitivedegree=dict_abc_order=33gn=mlemma=ἄγγελοςlemma_dictform=ἄγγελος, -ου, ὁlemma_freq=175lemma_gloss=angel, messengerlemma_strongs=32lemma_translit=aggelosmood=morph=N- ----GSM-normalized_word=ἀγγέλουnu=sgorig_order=127744ps=voice=vrsnum=23vt=
αὐτοῦ
case=genitivedegree=dict_abc_order=837gn=mlemma=αὐτόςlemma_dictform=αὐτός, -ή, -όlemma_freq=5546lemma_gloss=he, she, it, they, them, samelemma_strongs=846lemma_translit=autosmood=morph=RP ----GSM-normalized_word=αὐτοῦnu=sgorig_order=127745ps=voice=vrsnum=24vt=
τῷ
case=dativedegree=dict_abc_order=3455gn=mlemma=ὁlemma_dictform=ὁ, ἡ, τόlemma_freq=19769lemma_gloss=thelemma_strongs=3588lemma_translit=omood=morph=RA ----DSM-normalized_word=τῷnu=sgorig_order=127746ps=voice=vrsnum=25vt=
δούλῳ
case=dativedegree=dict_abc_order=1399gn=mlemma=δοῦλοςlemma_dictform=δούλη, -ης, ἡlemma_freq=126lemma_gloss=female slave, bonds-maidlemma_strongs=1399lemma_translit=doulosmood=morph=N- ----DSM-normalized_word=δούλῳnu=sgorig_order=127747ps=voice=vrsnum=26vt=
αὐτοῦ
case=genitivedegree=dict_abc_order=837gn=mlemma=αὐτόςlemma_dictform=αὐτός, -ή, -όlemma_freq=5546lemma_gloss=he, she, it, they, them, samelemma_strongs=846lemma_translit=autosmood=morph=RP ----GSM-normalized_word=αὐτοῦnu=sgorig_order=127748ps=voice=vrsnum=27vt=
Ἰωάννῃ
case=dativedegree=dict_abc_order=2470gn=mlemma=Ἰωάννηςlemma_dictform=Ἰωάννης, -ου, ὁlemma_freq=135lemma_gloss=Johnlemma_strongs=2491lemma_translit=Ioannesmood=morph=N- ----DSM-normalized_word=Ἰωάννῃnu=sgorig_order=127749ps=voice=vrsnum=28vt=
In [446]:
TestimonyOfJesus = '''
clause
w1:word lemma=μαρτυρία
w2:word lemma=Ἰησοῦς case=genitive
w1 <: w2
'''
TestimonyOfJesus=SBLGNT.search(TestimonyOfJesus)
SBLGNT.show(TestimonyOfJesus, start=1, end=10, condensed=True, colorMap={2: 'red', 3: 'orange'})
0.28s 6 results
verse 1
Revelation 1:2
verse
clause
ὅς
case=nominativelemma=ὅς
ἐμαρτύρησε(ν)
case=lemma=μαρτυρέω
τόν
case=accusativelemma=ὁ
λόγον
case=accusativelemma=λόγος
τοῦ
case=genitivelemma=ὁ
θεοῦ
case=genitivelemma=θεός
καί
case=lemma=καί
τήν
case=accusativelemma=ὁ
μαρτυρίαν
case=accusativelemma=μαρτυρία
Ἰησοῦ
case=genitivelemma=Ἰησοῦς
Χριστοῦ
case=genitivelemma=Χριστός
clause
ὅσα
case=accusativelemma=ὅσος
εἶδε(ν)
case=lemma=ὁράω
verse 2
Revelation 1:9
verse
clause
ἐγώ
case=nominativelemma=ἐγώ
Ἰωάννης
case=nominativelemma=Ἰωάννης
clause
ὁ
case=nominativelemma=ὁ
ἀδελφός
case=nominativelemma=ἀδελφός
ὑμῶν
case=genitivelemma=σύ
καί
case=lemma=καί
συγκοινωνός
case=nominativelemma=συγκοινωνός
ἐν
case=lemma=ἐν
τῇ
case=dativelemma=ὁ
θλίψει
case=dativelemma=θλῖψις
καί
case=lemma=καί
βασιλείᾳ
case=dativelemma=βασιλεία
καί
case=lemma=καί
ὑπομονῇ
case=dativelemma=ὑπομονή
ἐν
case=lemma=ἐν
Ἰησοῦ
case=dativelemma=Ἰησοῦς
clause
ἐγενόμην
case=lemma=γίνομαι
ἐν
case=lemma=ἐν
τῇ
case=dativelemma=ὁ
νήσῳ
case=dativelemma=νῆσος
τῇ
case=dativelemma=ὁ
καλουμένῃ
case=dativelemma=καλέω
Πάτμῳ
case=dativelemma=Πάτμος
διά
case=lemma=διά
τόν
case=accusativelemma=ὁ
λόγον
case=accusativelemma=λόγος
τοῦ
case=genitivelemma=ὁ
θεοῦ
case=genitivelemma=θεός
καί
case=lemma=καί
τήν
case=accusativelemma=ὁ
μαρτυρίαν
case=accusativelemma=μαρτυρία
Ἰησοῦ
case=genitivelemma=Ἰησοῦς
verse 3
Revelation 12:17
verse
clause
καί
case=lemma=καί
ὠργίσθη
case=lemma=ὀργίζομαι
ὁ
case=nominativelemma=ὁ
δράκων
case=nominativelemma=δράκων
ἐπί
case=lemma=ἐπί
τῇ
case=dativelemma=ὁ
γυναικί
case=dativelemma=γυνή
clause
καί
case=lemma=καί
ἀπῆλθε(ν)
case=lemma=ἀπέρχομαι
ποιῆσαι
case=lemma=ποιέω
πόλεμον
case=accusativelemma=πόλεμος
μετά
case=lemma=μετά
τῶν
case=genitivelemma=ὁ
λοιπῶν
case=genitivelemma=λοιπός
τοῦ
case=genitivelemma=ὁ
σπέρματος
case=genitivelemma=σπέρμα
αὐτῆς
case=genitivelemma=αὐτός
clause
τῶν
case=genitivelemma=ὁ
τηρούντων
case=genitivelemma=τηρέω
τάς
case=accusativelemma=ὁ
ἐντολάς
case=accusativelemma=ἐντολή
τοῦ
case=genitivelemma=ὁ
θεοῦ
case=genitivelemma=θεός
καί
case=lemma=καί
ἐχόντων
case=genitivelemma=ἔχω
τήν
case=accusativelemma=ὁ
μαρτυρίαν
case=accusativelemma=μαρτυρία
Ἰησοῦ
case=genitivelemma=Ἰησοῦς
verse 4
Revelation 19:10
verse
clause
καί
case=lemma=καί
ἔπεσα
case=lemma=πίπτω
ἔμπροσθεν
case=lemma=ἔμπροσθεν
τῶν
case=genitivelemma=ὁ
ποδῶν
case=genitivelemma=πούς
αὐτοῦ
case=genitivelemma=αὐτός
προσκυνῆσαι
case=lemma=προσκυνέω
αὐτῷ
case=dativelemma=αὐτός
clause
καί
case=lemma=καί
λέγει
case=lemma=λέγω
μοι
case=dativelemma=ἐγώ
clause
ὅρα
case=lemma=ὁράω
μή
case=lemma=μή
clause
σύνδουλος
case=nominativelemma=σύνδουλος
σου
case=genitivelemma=σύ
εἰμί
case=lemma=εἰμί
καί
case=lemma=καί
τῶν
case=genitivelemma=ὁ
ἀδελφῶν
case=genitivelemma=ἀδελφός
σου
case=genitivelemma=σύ
τῶν
case=genitivelemma=ὁ
ἐχόντων
case=genitivelemma=ἔχω
τήν
case=accusativelemma=ὁ
μαρτυρίαν
case=accusativelemma=μαρτυρία
Ἰησοῦ
case=genitivelemma=Ἰησοῦς
clause
τῷ
case=dativelemma=ὁ
θεῷ
case=dativelemma=θεός
προσκύνησον
case=lemma=προσκυνέω
clause
ἡ
case=nominativelemma=ὁ
γάρ
case=lemma=γάρ
μαρτυρία
case=nominativelemma=μαρτυρία
Ἰησοῦ
case=genitivelemma=Ἰησοῦς
ἐστί(ν)
case=lemma=εἰμί
τό
case=nominativelemma=ὁ
πνεῦμα
case=nominativelemma=πνεῦμα
τῆς
case=genitivelemma=ὁ
προφητείας
case=genitivelemma=προφητεία
verse 5
Revelation 20:4
verse
clause
καί
case=lemma=καί
εἶδον
case=lemma=ὁράω
θρόνους
case=accusativelemma=θρόνος
clause
καί
case=lemma=καί
ἐκάθισαν
case=lemma=καθίζω
ἐπί
case=lemma=ἐπί
αὐτούς
case=accusativelemma=αὐτός
clause
καί
case=lemma=καί
κρίμα
case=nominativelemma=κρίμα
ἐδόθη
case=lemma=δίδωμι
αὐτοῖς
case=dativelemma=αὐτός
clause
καί
case=lemma=καί
τάς
case=accusativelemma=ὁ
ψυχάς
case=accusativelemma=ψυχή
τῶν
case=genitivelemma=ὁ
πεπελεκισμένων
case=genitivelemma=πελεκίζω
διά
case=lemma=διά
τήν
case=accusativelemma=ὁ
μαρτυρίαν
case=accusativelemma=μαρτυρία
Ἰησοῦ
case=genitivelemma=Ἰησοῦς
καί
case=lemma=καί
διά
case=lemma=διά
τόν
case=accusativelemma=ὁ
λόγον
case=accusativelemma=λόγος
τοῦ
case=genitivelemma=ὁ
θεοῦ
case=genitivelemma=θεός
clause
καί
case=lemma=καί
οἵτινες
case=nominativelemma=ὅστις
οὐ
case=lemma=οὐ
προσεκύνησαν
case=lemma=προσκυνέω
τό
case=accusativelemma=ὁ
θηρίον
case=accusativelemma=θηρίον
οὐδέ
case=lemma=οὐδέ
τήν
case=accusativelemma=ὁ
εἰκόνα
case=accusativelemma=εἰκών
αὐτοῦ
case=genitivelemma=αὐτός
καί
case=lemma=καί
οὐ
case=lemma=οὐ
ἔλαβον
case=lemma=λαμβάνω
τό
case=accusativelemma=ὁ
χάραγμα
case=accusativelemma=χάραγμα
ἐπί
case=lemma=ἐπί
τό
case=accusativelemma=ὁ
μέτωπον
case=accusativelemma=μέτωπον
καί
case=lemma=καί
ἐπί
case=lemma=ἐπί
τήν
case=accusativelemma=ὁ
χεῖρα
case=accusativelemma=χείρ
αὐτῶν
case=genitivelemma=αὐτός
clause
καί
case=lemma=καί
ἔζησαν
case=lemma=ζάω
καί
case=lemma=καί
ἐβασίλευσαν
case=lemma=βασιλεύω
μετά
case=lemma=μετά
τοῦ
case=genitivelemma=ὁ
Χριστοῦ
case=genitivelemma=Χριστός
χίλια
case=accusativelemma=χίλιοι
ἔτη
case=accusativelemma=ἔτος
Complex searches¶
Lets search for corellated conjunctions¶
In Col 2:16-17 we find a syntactical construction that involves a series of conjunctions (καί and ἤ). The critical edition of the NA28 presents the text in the following way:
The syntactical structure is differently analyzed by scholars. Below you see a comparison between the cascadia and opentext analysis. One needs to keep in mind that the NA edition renders the text slightly different (see ἤ => καί) than the Mehrheitstext that is followed by the SBL edition (see καί => ἤ) :
Lets look first at the conjunction sequence that we have in the NA28. Does this appear elsewhere in the SBLGNT text?
In [447]:
KaiHeHe ='''
v1:verse
w1:word
w2:word lemma=καί
w3:word lemma=ἤ
w4:word lemma=ἤ
w1 < w2
w2 < w3
w3 < w4
'''
KaiHeHe=SBLGNT.search(KaiHeHe)
SBLGNT.show(KaiHeHe, start=1, end=20, condensed=True, colorMap={2: 'grey', 3: 'red', 4: 'magenta', 5: 'magenta'})
0.50s 114 results
verse 1
Matthew 17:25
verse
clause
λέγει
lemma=λέγω
clause
ναί
lemma=ναί
clause
καί
lemma=καί
ἐλθόντα
lemma=ἔρχομαι
εἰς
lemma=εἰς
τήν
lemma=ὁ
οἰκίαν
lemma=οἰκία
προέφθασε(ν)
lemma=προφθάνω
αὐτόν
lemma=αὐτός
ὁ
lemma=ὁ
Ἰησοῦς
lemma=Ἰησοῦς
λέγων
lemma=λέγω
clause
τί
lemma=τίς
σοι
lemma=σύ
δοκεῖ
lemma=δοκέω
clause
Σίμων
lemma=Σίμων
clause
οἱ
lemma=ὁ
βασιλεῖς
lemma=βασιλεύς
τῆς
lemma=ὁ
γῆς
lemma=γῆ
ἀπό
lemma=ἀπό
τίνων
lemma=τίς
λαμβάνουσι(ν)
lemma=λαμβάνω
τέλη
lemma=τέλος
ἤ
lemma=ἤ
κῆνσον
lemma=κῆνσος
clause
ἀπό
lemma=ἀπό
τῶν
lemma=ὁ
υἱῶν
lemma=υἱός
αὐτῶν
lemma=αὐτός
ἤ
lemma=ἤ
ἀπό
lemma=ἀπό
τῶν
lemma=ὁ
ἀλλοτρίων
lemma=ἀλλότριος
verse 2
Matthew 18:8
verse
clause
εἰ
lemma=εἰ
δέ
lemma=δέ
ἡ
lemma=ὁ
χείρ
lemma=χείρ
σου
lemma=σύ
ἤ
lemma=ἤ
ὁ
lemma=ὁ
πούς
lemma=πούς
σου
lemma=σύ
σκανδαλίζει
lemma=σκανδαλίζω
σε
lemma=σύ
clause
ἔκκοψον
lemma=ἐκκόπτω
αὐτόν
lemma=αὐτός
καί
lemma=καί
βάλε
lemma=βάλλω
ἀπό
lemma=ἀπό
σοῦ
lemma=σύ
clause
καλόν
lemma=καλός
σοι
lemma=σύ
ἐστί(ν)
lemma=εἰμί
εἰσελθεῖν
lemma=εἰσέρχομαι
εἰς
lemma=εἰς
τήν
lemma=ὁ
ζωήν
lemma=ζωή
κυλλόν
lemma=κυλλός
ἤ
lemma=ἤ
χωλόν
lemma=χωλός
clause
ἤ
lemma=ἤ
δύο
lemma=δύο
χεῖρας
lemma=χείρ
ἤ
lemma=ἤ
δύο
lemma=δύο
πόδας
lemma=πούς
ἔχοντα
lemma=ἔχω
βληθῆναι
lemma=βάλλω
εἰς
lemma=εἰς
τό
lemma=ὁ
πῦρ
lemma=πῦρ
τό
lemma=ὁ
αἰώνιον
lemma=αἰώνιος
verse 3
Matthew 25:44
verse
clause
τότε
lemma=τότε
ἀποκριθήσονται
lemma=ἀποκρίνομαι
καί
lemma=καί
αὐτοί
lemma=αὐτός
λέγοντες
lemma=λέγω
clause
κύριε
lemma=κύριος
clause
πότε
lemma=πότε
σε
lemma=σύ
εἴδομεν
lemma=ὁράω
πεινῶντα
lemma=πεινάω
ἤ
lemma=ἤ
διψῶντα
lemma=διψάω
ἤ
lemma=ἤ
ξένον
lemma=ξένος
ἤ
lemma=ἤ
γυμνόν
lemma=γυμνός
ἤ
lemma=ἤ
ἀσθενῆ
lemma=ἀσθενής
ἤ
lemma=ἤ
ἐν
lemma=ἐν
φυλακῇ
lemma=φυλακή
καί
lemma=καί
οὐ
lemma=οὐ
διηκονήσαμεν
lemma=διακονέω
σοι
lemma=σύ
verse 4
Mark 12:14
verse
clause
καί
lemma=καί
ἐλθόντες
lemma=ἔρχομαι
λέγουσι(ν)
lemma=λέγω
αὐτῷ
lemma=αὐτός
clause
διδάσκαλε
lemma=διδάσκαλος
clause
οἴδαμεν
lemma=οἶδα
ὅτι
lemma=ὅτι
ἀληθής
lemma=ἀληθής
εἶ
lemma=εἰμί
καί
lemma=καί
οὐ
lemma=οὐ
μέλει
lemma=μέλει
σοι
lemma=σύ
περί
lemma=περί
οὐδενός
lemma=οὐδείς
clause
οὐ
lemma=οὐ
γάρ
lemma=γάρ
βλέπεις
lemma=βλέπω
εἰς
lemma=εἰς
πρόσωπον
lemma=πρόσωπον
ἀνθρώπων
lemma=ἄνθρωπος
clause
ἀλλά
lemma=ἀλλά
ἐπί
lemma=ἐπί
ἀληθείας
lemma=ἀλήθεια
τήν
lemma=ὁ
ὁδόν
lemma=ὁδός
τοῦ
lemma=ὁ
θεοῦ
lemma=θεός
διδάσκεις
lemma=διδάσκω
clause
ἔξεστι(ν)
lemma=ἔξεστι(ν)
δοῦναι
lemma=δίδωμι
κῆνσον
lemma=κῆνσος
Καίσαρι
lemma=Καῖσαρ
ἤ
lemma=ἤ
οὔ
lemma=οὔ
clause
δῶμεν
lemma=δίδωμι
ἤ
lemma=ἤ
μή
lemma=μή
δῶμεν
lemma=δίδωμι
verse 5
Luke 12:11
verse
clause
ὅταν
lemma=ὅταν
δέ
lemma=δέ
εἰσφέρωσι(ν)
lemma=εἰσφέρω
ὑμᾶς
lemma=σύ
ἐπί
lemma=ἐπί
τάς
lemma=ὁ
συναγωγάς
lemma=συναγωγή
καί
lemma=καί
τάς
lemma=ὁ
ἀρχάς
lemma=ἀρχή
καί
lemma=καί
τάς
lemma=ὁ
ἐξουσίας
lemma=ἐξουσία
clause
μή
lemma=μή
μεριμνήσητε
lemma=μεριμνάω
πῶς
lemma=πῶς
ἤ
lemma=ἤ
τί
lemma=τίς
ἀπολογήσησθε
lemma=ἀπολογέομαι
ἤ
lemma=ἤ
τί
lemma=τίς
εἴπητε
lemma=λέγω
verse 6
Acts 17:21
verse
clause
Ἀθηναῖοι
lemma=Ἀθηναῖος
δέ
lemma=δέ
πάντες
lemma=πᾶς
καί
lemma=καί
οἱ
lemma=ὁ
ἐπιδημοῦντες
lemma=ἐπιδημέω
ξένοι
lemma=ξένος
εἰς
lemma=εἰς
οὐδέν
lemma=οὐδείς
ἕτερον
lemma=ἕτερος
ηὐκαίρουν
lemma=εὐκαιρέω
ἤ
lemma=ἤ
λέγειν
lemma=λέγω
τὶ
lemma=τις
ἤ
lemma=ἤ
ἀκούειν
lemma=ἀκούω
τὶ
lemma=τις
καινότερον
lemma=καινός
verse 7
1_Timothy 2:9
verse
clause
ὡσαύτως
lemma=ὡσαύτως
καί
lemma=καί
γυναῖκας
lemma=γυνή
ἐν
lemma=ἐν
καταστολῇ
lemma=καταστολή
κοσμίῳ
lemma=κόσμιος
μετά
lemma=μετά
αἰδοῦς
lemma=αἰδώς
καί
lemma=καί
σωφροσύνης
lemma=σωφροσύνη
κοσμεῖν
lemma=κοσμέω
ἑαυτάς
lemma=ἑαυτοῦ
clause
μή
lemma=μή
ἐν
lemma=ἐν
πλέγμασι(ν)
lemma=πλέγμα
καί
lemma=καί
χρυσίῳ
lemma=χρυσίον
ἤ
lemma=ἤ
μαργαρίταις
lemma=μαργαρίτης
ἤ
lemma=ἤ
ἱματισμῷ
lemma=ἱματισμός
πολυτελεῖ
lemma=πολυτελής
In [448]:
KaiHeHe2 ='''
v1:verse
w1:word
w2:word lemma=καί
w3:word lemma=ἤ
w4:word lemma=ἤ
v1 =20: w1
w1 < w2
w1 <10: w2
w2 < w3
w2 <6: w3
w3 < w4
w3 <6: w4
'''
KaiHeHe2=SBLGNT.search(KaiHeHe2)
SBLGNT.show(KaiHeHe2, start=1, end=200, condensed=True, colorMap={2: 'grey', 3: 'red', 4: 'magenta', 5: 'magenta'})
0.49s 21 results
verse 1
Luke 12:11
verse
clause
ὅταν
lemma=ὅταν
δέ
lemma=δέ
εἰσφέρωσι(ν)
lemma=εἰσφέρω
ὑμᾶς
lemma=σύ
ἐπί
lemma=ἐπί
τάς
lemma=ὁ
συναγωγάς
lemma=συναγωγή
καί
lemma=καί
τάς
lemma=ὁ
ἀρχάς
lemma=ἀρχή
καί
lemma=καί
τάς
lemma=ὁ
ἐξουσίας
lemma=ἐξουσία
clause
μή
lemma=μή
μεριμνήσητε
lemma=μεριμνάω
πῶς
lemma=πῶς
ἤ
lemma=ἤ
τί
lemma=τίς
ἀπολογήσησθε
lemma=ἀπολογέομαι
ἤ
lemma=ἤ
τί
lemma=τίς
εἴπητε
lemma=λέγω
verse 2
1_Timothy 2:9
verse
clause
ὡσαύτως
lemma=ὡσαύτως
καί
lemma=καί
γυναῖκας
lemma=γυνή
ἐν
lemma=ἐν
καταστολῇ
lemma=καταστολή
κοσμίῳ
lemma=κόσμιος
μετά
lemma=μετά
αἰδοῦς
lemma=αἰδώς
καί
lemma=καί
σωφροσύνης
lemma=σωφροσύνη
κοσμεῖν
lemma=κοσμέω
ἑαυτάς
lemma=ἑαυτοῦ
clause
μή
lemma=μή
ἐν
lemma=ἐν
πλέγμασι(ν)
lemma=πλέγμα
καί
lemma=καί
χρυσίῳ
lemma=χρυσίον
ἤ
lemma=ἤ
μαργαρίταις
lemma=μαργαρίτης
ἤ
lemma=ἤ
ἱματισμῷ
lemma=ἱματισμός
πολυτελεῖ
lemma=πολυτελής
No "either ... or" construction in:
- Mat 6:31 (NA28)
- Mark 10:29
- Luk 18:29
- Rom 8:35
- 1 Cor 5:11 (its not the first ἢ but the previous ἐάν that initiates the "either")
- 1 Cor 14:6 (its not the first ἢ but the previous ἐάν that initiates the "either")
- 1 Thes 2:19
- 1 Peter 4:15
The only exception is Mk 13:35. But the construction is not triggering the "either ... or" function (see also modern translations). It rather hints at a textcritical issue which is also well documented in the text critical apparatus:
Consequently, the proper translation is "X or Y or Z...".
Conclusion: it is highly unlikely that Col 2:16 resembles an "either ... or" construction. By default if there are three ἢ conjunctions appearing in a sequence they trigger the meaning "or ... or".
The following query does not exclude a preceding καὶ:
In [449]:
HeHeHe ='''
verse
word lemma=ἤ
< word lemma=ἤ
< word lemma=ἤ
'''
HeHeHe=SBLGNT.search(HeHeHe)
SBLGNT.show(HeHeHe, start=1, end=10, condensed=True, colorMap={2: 'magenta', 3: 'magenta', 4: 'magenta'})
0.44s 117 results
verse 1
Matthew 18:8
verse
clause
εἰ
lemma=εἰ
δέ
lemma=δέ
ἡ
lemma=ὁ
χείρ
lemma=χείρ
σου
lemma=σύ
ἤ
lemma=ἤ
ὁ
lemma=ὁ
πούς
lemma=πούς
σου
lemma=σύ
σκανδαλίζει
lemma=σκανδαλίζω
σε
lemma=σύ
clause
ἔκκοψον
lemma=ἐκκόπτω
αὐτόν
lemma=αὐτός
καί
lemma=καί
βάλε
lemma=βάλλω
ἀπό
lemma=ἀπό
σοῦ
lemma=σύ
clause
καλόν
lemma=καλός
σοι
lemma=σύ
ἐστί(ν)
lemma=εἰμί
εἰσελθεῖν
lemma=εἰσέρχομαι
εἰς
lemma=εἰς
τήν
lemma=ὁ
ζωήν
lemma=ζωή
κυλλόν
lemma=κυλλός
ἤ
lemma=ἤ
χωλόν
lemma=χωλός
clause
ἤ
lemma=ἤ
δύο
lemma=δύο
χεῖρας
lemma=χείρ
ἤ
lemma=ἤ
δύο
lemma=δύο
πόδας
lemma=πούς
ἔχοντα
lemma=ἔχω
βληθῆναι
lemma=βάλλω
εἰς
lemma=εἰς
τό
lemma=ὁ
πῦρ
lemma=πῦρ
τό
lemma=ὁ
αἰώνιον
lemma=αἰώνιος
verse 2
Matthew 19:29
verse
clause
καί
lemma=καί
πᾶς
lemma=πᾶς
ὅστις
lemma=ὅστις
ἀφῆκε(ν)
lemma=ἀφίημι
οἰκίας
lemma=οἰκία
ἤ
lemma=ἤ
ἀδελφούς
lemma=ἀδελφός
ἤ
lemma=ἤ
ἀδελφάς
lemma=ἀδελφή
ἤ
lemma=ἤ
πατέρα
lemma=πατήρ
ἤ
lemma=ἤ
μητέρα
lemma=μήτηρ
ἤ
lemma=ἤ
γυναῖκα
lemma=γυνή
ἤ
lemma=ἤ
τέκνα
lemma=τέκνον
ἤ
lemma=ἤ
ἀγρούς
lemma=ἀγρός
ἕνεκεν
lemma=ἕνεκεν
τοῦ
lemma=ὁ
ὀνόματος
lemma=ὄνομα
μου
lemma=ἐγώ
clause
ἑκατονταπλασίονα
lemma=ἑκατονταπλασίων
λήμψεται
lemma=λαμβάνω
καί
lemma=καί
ζωήν
lemma=ζωή
αἰώνιον
lemma=αἰώνιος
κληρονομήσει
lemma=κληρονομέω
verse 3
Matthew 25:44
verse
clause
τότε
lemma=τότε
ἀποκριθήσονται
lemma=ἀποκρίνομαι
καί
lemma=καί
αὐτοί
lemma=αὐτός
λέγοντες
lemma=λέγω
clause
κύριε
lemma=κύριος
clause
πότε
lemma=πότε
σε
lemma=σύ
εἴδομεν
lemma=ὁράω
πεινῶντα
lemma=πεινάω
ἤ
lemma=ἤ
διψῶντα
lemma=διψάω
ἤ
lemma=ἤ
ξένον
lemma=ξένος
ἤ
lemma=ἤ
γυμνόν
lemma=γυμνός
ἤ
lemma=ἤ
ἀσθενῆ
lemma=ἀσθενής
ἤ
lemma=ἤ
ἐν
lemma=ἐν
φυλακῇ
lemma=φυλακή
καί
lemma=καί
οὐ
lemma=οὐ
διηκονήσαμεν
lemma=διακονέω
σοι
lemma=σύ
verse 4
Mark 10:29
verse
clause
ἔφη
lemma=φημί
ὁ
lemma=ὁ
Ἰησοῦς
lemma=Ἰησοῦς
clause
ἀμήν
lemma=ἀμήν
λέγω
lemma=λέγω
ὑμῖν
lemma=σύ
clause
οὐδείς
lemma=οὐδείς
ἐστί(ν)
lemma=εἰμί
ὅς
lemma=ὅς
ἀφῆκε(ν)
lemma=ἀφίημι
οἰκίαν
lemma=οἰκία
ἤ
lemma=ἤ
ἀδελφούς
lemma=ἀδελφός
ἤ
lemma=ἤ
ἀδελφάς
lemma=ἀδελφή
ἤ
lemma=ἤ
μητέρα
lemma=μήτηρ
ἤ
lemma=ἤ
πατέρα
lemma=πατήρ
ἤ
lemma=ἤ
τέκνα
lemma=τέκνον
ἤ
lemma=ἤ
ἀγρούς
lemma=ἀγρός
ἕνεκεν
lemma=ἕνεκεν
ἐμοῦ
lemma=ἐγώ
καί
lemma=καί
ἕνεκεν
lemma=ἕνεκεν
τοῦ
lemma=ὁ
εὐαγγελίου
lemma=εὐαγγέλιον
verse 5
Mark 13:35
verse
clause
γρηγορεῖτε
lemma=γρηγορέω
οὖν
lemma=οὖν
clause
οὐ
lemma=οὐ
οἴδατε
lemma=οἶδα
γάρ
lemma=γάρ
πότε
lemma=πότε
ὁ
lemma=ὁ
κύριος
lemma=κύριος
τῆς
lemma=ὁ
οἰκίας
lemma=οἰκία
ἔρχεται
lemma=ἔρχομαι
clause
ἤ
lemma=ἤ
ὀψέ
lemma=ὀψέ
ἤ
lemma=ἤ
μεσονύκτιον
lemma=μεσονύκτιον
ἤ
lemma=ἤ
ἀλεκτοροφωνίας
lemma=ἀλεκτοροφωνία
ἤ
lemma=ἤ
πρωΐ
lemma=πρωΐ
verse 6
Luke 18:29
verse
clause
ὁ
lemma=ὁ
δέ
lemma=δέ
εἶπε(ν)
lemma=λέγω
αὐτοῖς
lemma=αὐτός
clause
ἀμήν
lemma=ἀμήν
λέγω
lemma=λέγω
ὑμῖν
lemma=σύ
ὅτι
lemma=ὅτι
οὐδείς
lemma=οὐδείς
ἐστί(ν)
lemma=εἰμί
ὅς
lemma=ὅς
ἀφῆκε(ν)
lemma=ἀφίημι
οἰκίαν
lemma=οἰκία
ἤ
lemma=ἤ
γυναῖκα
lemma=γυνή
ἤ
lemma=ἤ
ἀδελφούς
lemma=ἀδελφός
ἤ
lemma=ἤ
γονεῖς
lemma=γονεύς
ἤ
lemma=ἤ
τέκνα
lemma=τέκνον
ἕνεκεν
lemma=ἕνεκεν
τῆς
lemma=ὁ
βασιλείας
lemma=βασιλεία
τοῦ
lemma=ὁ
θεοῦ
lemma=θεός
verse 7
Romans 8:35
verse
clause
τίς
lemma=τίς
ἡμᾶς
lemma=ἐγώ
χωρίσει
lemma=χωρίζω
ἀπό
lemma=ἀπό
τῆς
lemma=ὁ
ἀγάπης
lemma=ἀγάπη
τοῦ
lemma=ὁ
Χριστοῦ
lemma=Χριστός
clause
θλῖψις
lemma=θλῖψις
ἤ
lemma=ἤ
στενοχωρία
lemma=στενοχωρία
ἤ
lemma=ἤ
διωγμός
lemma=διωγμός
ἤ
lemma=ἤ
λιμός
lemma=λιμός
ἤ
lemma=ἤ
γυμνότης
lemma=γυμνότης
ἤ
lemma=ἤ
κίνδυνος
lemma=κίνδυνος
ἤ
lemma=ἤ
μάχαιρα
lemma=μάχαιρα
verse 8
1_Corinthians 5:11
verse
clause
νῦν
lemma=νῦν
δέ
lemma=δέ
ἔγραψα
lemma=γράφω
ὑμῖν
lemma=σύ
μή
lemma=μή
συναναμίγνυσθαι
lemma=συναναμίγνυμι
ἐάν
lemma=ἐάν
τὶς
lemma=τις
ἀδελφός
lemma=ἀδελφός
ὀνομαζόμενος
lemma=ὀνομάζω
ᾖ
lemma=εἰμί
πόρνος
lemma=πόρνος
ἤ
lemma=ἤ
πλεονέκτης
lemma=πλεονέκτης
ἤ
lemma=ἤ
εἰδωλολάτρης
lemma=εἰδωλολάτρης
ἤ
lemma=ἤ
λοίδορος
lemma=λοίδορος
ἤ
lemma=ἤ
μέθυσος
lemma=μέθυσος
ἤ
lemma=ἤ
ἅρπαξ
lemma=ἅρπαξ
clause
τῷ
lemma=ὁ
τοιούτῳ
lemma=τοιοῦτος
μηδέ
lemma=μηδέ
συνεσθίειν
lemma=συνεσθίω
verse 9
1_Corinthians 14:6
verse
clause
νῦν
lemma=νῦν
δέ
lemma=δέ
clause
ἀδελφοί
lemma=ἀδελφός
clause
ἐάν
lemma=ἐάν
ἔλθω
lemma=ἔρχομαι
πρός
lemma=πρός
ὑμᾶς
lemma=σύ
γλώσσαις
lemma=γλῶσσα
λαλῶν
lemma=λαλέω
clause
τί
lemma=τίς
ὑμᾶς
lemma=σύ
ὠφελήσω
lemma=ὠφελέω
clause
ἐάν
lemma=ἐάν
μή
lemma=μή
ὑμῖν
lemma=σύ
λαλήσω
lemma=λαλέω
ἤ
lemma=ἤ
ἐν
lemma=ἐν
ἀποκαλύψει
lemma=ἀποκάλυψις
ἤ
lemma=ἤ
ἐν
lemma=ἐν
γνώσει
lemma=γνῶσις
ἤ
lemma=ἤ
ἐν
lemma=ἐν
προφητείᾳ
lemma=προφητεία
ἤ
lemma=ἤ
ἐν
lemma=ἐν
διδαχῇ
lemma=διδαχή
verse 10
Colossians 2:16
verse
clause
μή
lemma=μή
οὖν
lemma=οὖν
τὶς
lemma=τις
ὑμᾶς
lemma=σύ
κρινέτω
lemma=κρίνω
ἐν
lemma=ἐν
βρώσει
lemma=βρῶσις
ἤ
lemma=ἤ
ἐν
lemma=ἐν
πόσει
lemma=πόσις
ἤ
lemma=ἤ
ἐν
lemma=ἐν
μέρει
lemma=μέρος
ἑορτῆς
lemma=ἑορτή
ἤ
lemma=ἤ
νουμηνίας
lemma=νουμηνία
ἤ
lemma=ἤ
σαββάτων
lemma=σάββατον
In [450]:
JesusInNT = '''
book
clause
word lemma=Ἰησοῦς case=nominative
'''
JesusInNT=SBLGNT.search(JesusInNT)
SBLGNT.show(JesusInNT, start=1, end=3, condensed=True, colorMap={2: 'gold'})
0.21s 456 results
verse 1
Matthew 1:16
verse
clause
Ἰακώβ
case=nominativelemma=Ἰακώβ
δέ
case=lemma=δέ
ἐγέννησε(ν)
case=lemma=γεννάω
τόν
case=accusativelemma=ὁ
Ἰωσήφ
case=accusativelemma=Ἰωσήφ
τόν
case=accusativelemma=ὁ
ἄνδρα
case=accusativelemma=ἀνήρ
Μαρίας
case=genitivelemma=Μαρία
clause
ἐκ
case=lemma=ἐκ
ἧς
case=genitivelemma=ὅς
ἐγεννήθη
case=lemma=γεννάω
Ἰησοῦς
case=nominativelemma=Ἰησοῦς
ὁ
case=nominativelemma=ὁ
λεγόμενος
case=nominativelemma=λέγω
Χριστός
case=nominativelemma=Χριστός
verse 2
Matthew 3:13
verse
clause
τότε
case=lemma=τότε
παραγίνεται
case=lemma=παραγίνομαι
ὁ
case=nominativelemma=ὁ
Ἰησοῦς
case=nominativelemma=Ἰησοῦς
ἀπό
case=lemma=ἀπό
τῆς
case=genitivelemma=ὁ
Γαλιλαίας
case=genitivelemma=Γαλιλαία
ἐπί
case=lemma=ἐπί
τόν
case=accusativelemma=ὁ
Ἰορδάνην
case=accusativelemma=Ἰορδάνης
πρός
case=lemma=πρός
τόν
case=accusativelemma=ὁ
Ἰωάννην
case=accusativelemma=Ἰωάννης
τοῦ
case=genitivelemma=ὁ
βαπτισθῆναι
case=lemma=βαπτίζω
ὑπό
case=lemma=ὑπό
αὐτοῦ
case=genitivelemma=αὐτός
verse 3
Matthew 3:15
verse
clause
ἀποκριθείς
case=nominativelemma=ἀποκρίνομαι
δέ
case=lemma=δέ
ὁ
case=nominativelemma=ὁ
Ἰησοῦς
case=nominativelemma=Ἰησοῦς
εἶπε(ν)
case=lemma=λέγω
πρός
case=lemma=πρός
αὐτόν
case=accusativelemma=αὐτός
clause
ἄφες
case=lemma=ἀφίημι
ἄρτι
case=lemma=ἄρτι
clause
οὕτω(ς)
case=lemma=οὕτω(ς)
γάρ
case=lemma=γάρ
πρέπον
case=nominativelemma=πρέπω
ἐστί(ν)
case=lemma=εἰμί
ἡμῖν
case=dativelemma=ἐγώ
πληρῶσαι
case=lemma=πληρόω
πᾶσαν
case=accusativelemma=πᾶς
δικαιοσύνην
case=accusativelemma=δικαιοσύνη
clause
τότε
case=lemma=τότε
ἀφίησι(ν)
case=lemma=ἀφίημι
αὐτόν
case=accusativelemma=αὐτός
In [451]:
SBLGNT.export(JesusInNT, toDir='D:/OneDrive/1200_AUS-research/Fabric-TEXT/0_data_SBLGNT/', toFile='JesusInNT.tsv')
In [452]:
JesusInNT=pd.read_csv('D:/OneDrive/1200_AUS-research/Fabric-TEXT/0_data_SBLGNT/JesusInNT.tsv',delimiter='\t',encoding='utf-16')
JesusInNT.head()
Out[452]:
| R | S1 | S2 | S3 | NODE1 | TYPE1 | NODE2 | TYPE2 | TEXT2 | NODE3 | TYPE3 | TEXT3 | case3 | lemma3 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1 | Matthew | 1 | 16 | 137555 | book | 137882 | clause | ἐκ ἧς ἐγεννήθη Ἰησοῦς ὁ λεγόμενος Χριστός | 243 | word | Ἰησοῦς | nominative | Ἰησοῦς |
| 1 | 2 | Matthew | 3 | 13 | 137555 | book | 137984 | clause | τότε παραγίνεται ὁ Ἰησοῦς ἀπό τῆς Γαλιλαίας ἐπ... | 1133 | word | Ἰησοῦς | nominative | Ἰησοῦς |
| 2 | 3 | Matthew | 3 | 15 | 137555 | book | 137988 | clause | ἀποκριθείς δέ ὁ Ἰησοῦς εἶπε(ν) πρός αὐτόν | 1167 | word | Ἰησοῦς | nominative | Ἰησοῦς |
| 3 | 4 | Matthew | 3 | 16 | 137555 | book | 137992 | clause | βαπτισθείς δέ ὁ Ἰησοῦς εὐθύς ἀνέβη ἀπό τοῦ ὕδα... | 1187 | word | Ἰησοῦς | nominative | Ἰησοῦς |
| 4 | 5 | Matthew | 4 | 1 | 137555 | book | 137998 | clause | τότε ὁ Ἰησοῦς ἀνήχθη εἰς τήν ἔρημον ὑπό τοῦ πν... | 1227 | word | Ἰησοῦς | nominative | Ἰησοῦς |
In [453]:
figure(num=None, figsize=(5, 5), dpi=80, facecolor='w', edgecolor='k')
JesusInNT.groupby("S1").size().sort_values(ascending=True).plot.barh()
plt.xlabel('occurence of Jesus as subject')
plt.ylabel('NT books')
plt.title('Jesus as subject (case=nominative)')
plt.show()
In [454]:
JesusInNT.S1.value_counts(sort=False).plot.pie(autopct='%1.0f%%', shadow=True, startangle=90)
plt.show()
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